Architecture & Protocol Design for Pervasive Robot Swarm Communication Networks

There has been increasing interest in deploying a team of robots, or robot swarms, to fulfill certain complicate tasks such as surveillance. Since robot swarms may move to areas of far distance, it is important to have a pervasive networking environment for communications amongrobots, administrators, and mobile users. In this paper, we first propose a pervasive architecture to integrate wireless mesh networks and robot swarm networks to build a robot swarm communication network within the areas of special interest. Under the proposed architecture, one or more robots can get connected with a nearby mesh router and access the remote server, while a self-organizing mobile ad hoc network is formed within each swarm for communicationsamongtherobots.

MIMO Architecture for Swarm Drone Communication

In the past few years, robot swarms have gained consid erable attention. Many complicated artificial intelligent algorithms proposed to improve the correctness and effi ciency of decision-making of a single robot. In addition, it has been found that using a team of robots has many advan tages. First, single robot may not be adequate to fulfill some tasks, especially in complicated geographical environments where multiple obstacles exist on the path of the robots. Second, using a team of robots can achieve better fault tolerance for mission-critical applications such as surveil lance and military battlefield where a single robot may be damaged or die due to, say, battery failure. Third, a swarm of robots is an appealing choice for accomplishing surveil lance tasks, which would be otherwise difficult for human beings, since it is low risk and flexible. Furthermore, the idea of robot swarm is very natural since most complicated tasks such as moving around a big obstacle and cargo trans portation require collaboration among the robots. Finally, withthecostofrobotsbeingsignificantlyreducedtoseveral hundred dollars, dispatching multiple robots is becoming a feasible scenario for a broader range of potential users.

Since robot swarms may move to areas of far distance, it is insufficient to deploy robot swarms directly with out communications and coordination. Instead, it is highly important to have a pervasive networking environment for communications among robots, administrators, and mobile users. To address this important issue, we present a per vasive architecture for swarm communication in this paper. Wethenidentifyandexamineseveralchallengingissuesfor enabling this architecture, e.g., autonomous swarm deploy ment while keeping swarm’s integrity and connectivity, swarm monitoring, and swarm control.

Swarm deployment is a challenging research problem. Onceateamofrobotsaredispatched to an unknownarea, it is important for the robots to move independently but also collaboratively, such that the swarm coverage can be max imized. In the case of a closed area, such as a room, robots should be able to adapt themselves to the shape of the area. Due to the nature of their autonomous behavior, it is non trivial to design an algorithm that can effectively maintain continuous swarm connectivity. In addition, the efficiency of a swarm highly depends on how fast it is deployed. If robots move slowly and randomly, the time overhead of deployment may be too high compared to the tight delay constraint of fulfilling certain tasks such as target tracking. Finally, mobile robots are battery operated, minimizing the moving distance leads to maximum energy efficiency and extended lifetime. Thus, it is desirable to devise a fast and energy efficient algorithm that deploys robot swarms with continuous connectivity and maximum coverage.

In a typical autonomous swarm deployment scenario, map information needs to be loaded in each robot before it explores an area. Alternatively, a team leader controls the movement of all other robots to achieve a wider coverage while maintaining connectivity. However, neither approach is suitable in practical scenarios where map information may not be available nor the robots may fail due to bat tery failure or outside attack. Actually, with the limited processing capability and battery power, low time com plexity and communication frequency are preferred for swarm deployment algorithms. To mitigate the challenges in swarmcommunication, weproposeaschemethatadopts a differentiated timer for each robot, such that each robot determines its action locally without consulting any central server or group leader inside the swarm. This feature makes the algorithm(s) scalable for various environments. Results show that this approach is fast and energy efficient com pared to a random-movement-based strategy. In addition, this scheme can be extended to fulfill other tasks such as undeployment, coordinated movements, etc.

To enable convenient and flexible swarm monitoring, control, and coordination within the proposed architecture, wehavedesignedanddevelopedROBOTRAK,ausefuland secure toolkit for monitoring, control, and coordination of intelligent robotic swarms. Using TCPconnections through a wireless mesh backbone, the ROBOTRAK server can exchange information with the robotic swarm reliably and continuously. For monitoring purpose, all the bots collect and report wireless signal strength, interference, neighbor ing bots list, and location information. For control purpose, a new robot can be dispatched to join a swarm or an exist ing robot in a swarm can be guided to move to a specific destination. For coordination purpose, the server maintains the connectivity of the swarm under various situations. Fur thermore, in order to maintain network privacy and avoid outsider intrusion, multi-security levels and adynamicpass word mechanism have been incorporated in the toolkit. Demonstrations show that the toolkit is very useful and effective for swarm monitoring.

.Inthisarchitecture,robots are clustered to one or multiple teams/swarms and each swarm can be monitored and controlled by some cen tral servers through a wireless mesh backbone as well as the Internet. Within each swarm, a self-organizing mobile ad hoc network (MANET) is formed such that all robots are connected to all other robots despite of movements. Meanwhile, mobile users can also monitor swarms through mobile devices such as laptops and PDAs and can take action immediately based on certain information collected. The monitoring module may collect location, topology, and other related information. Images and videos are also allowed to be streamed from robots to servers or mobile users for appropriate decisions.

The architecture of the proposed robot swarm commu nication network is illustrated in Figure 1. Wireless mesh routers are deployed on the top of buildings, walls, or towers. Each mesh router consists of multiple antennas and can operate over multiple channels to improve the network capacity and coverage. Mesh routers form a wireless back bone, which is further connected to wired Internet through gateways and IP routers. One or more teams of robots are deployed. Eachrobotisequippedwithoneormorewireless adaptors for communicating with mesh routers or other robots, digital video camera, and GPS. Each robot swarm maintains continuous connectivity for all of its robots and periodically updates the collected information such as its location, picture, and video to some administrators that reside either locally or remotely. Meanwhile, users such as security guards drive around the area frequently, access wireless devices such as PDA or Palm PC, and monitor one or more robot swarms. If certain emergency is identified, a user can immediately take action. Due to the limited com putational power of these devices on image/video analysis, a user may also get certain instructions from the admin istrators for appropriate actions. Altogether, the proposed architecture presents an interactive coordination framework for real-time monitoring, efficient collaboration, and fast reaction. Furthermore, since mesh routers, servers, robots, and PDAsare all inexpensive and require minimum human labor, such architecture is feasible and very cost effective.

With the proposed architecture, the robot swarms help fulfill the following critical tasks difficult for humans: (1) Continuous Surveillance — a swarmofrobotscanmove around various areas in a non-stopped pattern, which significantly improves the information accuracy and the timeliness of actions taken by security guards. Somerobots can be built at very small size and cannot be easily detected, thus enhancing the effectiveness of surveillance; (2) Information Collection — through effective coordination, a team of robots equipped with GPS, video camera, and sensors can capture image/video period ically, recognize sensitive objects such as enemy and chemical/biological stuff, and report to administrators or security guards instantly; and (3) Coverage Inspection — a robot can report to the admin istrator immediately if it cannot receive wireless signal from the mesh routers, which helps identify certain areas subject to security problems.

The proposed architecture poses new challenges from both protocol design and system development aspects. On the one hand, howtocoordinate robots that move randomly and continuously is a critical issue. Related issues include collaborative object recognition, deployment, etc. On the other hand, how to develop a software system to efficiently monitor,coordinate,andcontrolrobotswarmsinareal-time manner is important for the system to be put in practice. If robots can be effectively monitored, critical information suchasthelocationofeachrobot,theconnectivity,andother information can be collected at administrators or mobile users. Based on the information from the swarm, the server may perform certain control and/or coordination to assist the robots if necessary. In addition, streaming video from robots requires QoS support from the WMN backbone.

Fast Autonomous Swarm Deployment Algorithm

Usually robots need to exchange certain information such as location and status for decision-making in order to fulfill certain complicate tasks such as surveillance. The key chal lenge of this type of coordination is how to ensure swarm connectivitywithoutthepriorknowledgeaboutrobotmobil ity pattern, since robots can move freely based on its own intelligence and tasks. Compared to centralized approaches where a central control robot coordinates the movement of other robots, distributed approaches exhibits several advantages such as better scalability, efficiency, and fault tolerance. Based on collected information from neighbors through periodic messageexchanges,arobotcandetermine how to guide its own movement to achieve certain goals while maintaining effective connectivity.

Our proposed approach adopts a differentiated timer such that each robot determines its action locally without consulting any central server or group leader in the swarm. Coupled with a move mentadaptorandboundarydetector,thisapproachachieves distributed, fast, energy efficient, and scalable robot swarm deployment for various environments. The contributions of the proposed algorithm are: (1) differentiated timer eliminates many unnecessary movements and thus achieves shorter stabilization phase and less movement distance; (2) network connectivity is always maintained within the swarm; and (3) the proposed algorithm works well with both open and closed areas with various shapes.

Autonomous Deployment Algorithm

To coordinate robot movements and avoid unnecessary movements, we propose a differentiated-timer-based technique. The dura tion of the timer is determined based on a node’s neighbor information such that nodes on the edge of the swarm move f irst to accelerate the deployment process. Upon the expira tion of a timer, a robot is pushed by most critical neighbors so that it can quickly move to farther locations. The speed of the movement depends on its distances from each neigh bor but is chosen to make sure that the robot does not get disconnected from these neighbors. In addition, a special measure is taken to prevent certain robots in the center of the swarm from moving back and forth without stabilizing to a specific location.

In addition, we can reasonably assume that each robot is able to identify other peers in the same swarm. This can be easily realized by adding certain group identifier in the packet header. Whenever a robot receives a message from another robot, it will first check the group identifier and then decide whether or not to communicate with the sender. Note that GPS and wireless sensors/adaptors are now quite inexpensive and do not significantly increase the cost of robots.Toenablecommunicationsamongrobots,theswarm network can be configured to operate in the ad hoc mode.

The proposed algorithm consists of the following four components: (1) differential timer — due e to the autonomous nature of the wireless enabled robots, each robot follows a wait and-move mobility pattern where an action is taken after a self-tuning timer expires since the last action; (2) movement adapter — each hrobotdeterminewhichdirec tion and how fast it should move based on the distance from critical neighbors, transmission range, etc., while maintaining continuous connectivity within the swarm; (3) oscillation detector — after the swarm has been deployed, there is still a residual oscillation of certain nodes. Duetotherepulsion forces of the various walls, some robots may be forced to go back and forth for a long time. Detecting and handling this wobbling effect helps to maintain the battery life of involving robots; and (4) boundary detector — each robot needs to determine when a boundary is close and then continue gliding on the right direction

Differnetiation Timer

Once a robot swarm arrives at a specific area, robots should start to scatter out to cover the area. Now, the issue is who should move first. If all robots move at the same time, then it is possible that many unnecessary movements will occur. Figure 2 illustrates this situation. The best situation will be that robots at the border of the swarmmovefirst,fol lowed by movements of centering nodes.

Oscillation Detector

To addressthisproblem,weproposeasolutionasfollows. All robots keep a history of their last six movements. If a robot detects that it changes its moving direction more than twice, it identifies itself as wobbling and then reset its timer to twice the value of its last used timer. The more times a robot is identified to be oscillating, the greater the timer will be. In this way, we can ensure that the wobbling effect is reduced to a minimum.

However, it should be noted that for the function to work properly, six movements must have already occurred. Also, even if a robot is found to be oscillating, it will attempt to move again after its timer has expired. There may be a case where a robot will be able to move in the appropriate direc tion after it has beenclassifiedasanoscillatingrobot.Inthat case, the wobbling status flag is removed after it has suc cessfully performed six movements in the same direction.

Boundary Detector

Whenever a robot moves close to bound aries such as a wall, it simply glides along the wall based on which direction it is being pushed. However, the speed will be much lower to avoid the collision with the bound ary. In case a robot hits a boundary and gets stuck at a corner, it backs off and randomly chooses a direction to glide.

Monitoring Module

The monitoring module basically analyzes the messages from each robot and decides the desired information the administrator intends to track such as physical location. Since connectivity within the swarm is critical for the performance ofaswarm,eachrobotshouldreportitsneigh boring robots within the same swarm. This neighboring informationcanbeobtainedthroughcommunicationamong mobile robots in a self-organized wireless ad hoc network. In summary, each robot will report following basic infor mation to the server for monitoring purpose: (1) physical location — the coordinates obtained from the GPS system is sent from each robot to the server; and (2) neighbor list — whenever a robot receives or overhears messages from another robot in the same swarm, it will add the robot to its neighboring list. This list is periodicallyrefreshedtoremoveoldneighborsandadd new neighbors.

The most important issue is how to place mesh routers such that all targeted areas are covered suffi ciently and efficiently, i.e., all areas are covered with good signal strength with minimum number of mesh routers. Typically, some areas mayexperiencehighinterferenceand some areas may experience weak or no signal, due to inap propriate installation of many mesh routers.

Control Module

The control module is designed to only provide assistance to the swarm in certain unusual situations. As mentioned earlier, the control fromtheserverisnotsupposedtoreplace the basic operations of each individual robot or the whole swarm,but to help them: (1) robot joining — dispatching a new robot to join the swarmbyprovidingthe destination. While the robot is moving toward the team, the center location of swarm is periodically updated for guidance; (2) robot leaving — requesting one robot in a swarm to leave the swarm. The robot may go back to storage, or go to a location specified by the server; and (3) swarm movement — requesting the whole swarm to move to another location to perform some other tasks. There may be several reasons for this. The adminis trator may identify certain dangerous situations at the current location and thus wants the robots to move immediately. Or the administrator may have another urgent task for the swarm to fulfill.

Coordination Module

Similar to the control module, the coordination module is also designed to only provide minimum assistance for swarm connectivity. The basic coordination is still per formed by the swarm itself independently with certain intelligent algorithms; the following situations are conisidered: (1) isolation of a regular robot — if one robot is isolated from the rest of the team due to various reasons such as entering a building or the other side of an obstacle, it will report its neighbor list as empty to the server. Basedonthisinformation,the server can quicklydirect the robot to move toward the swarm with higher speed; (2) isolation of a leader robot — it is assumed that the leader should guide the whole swarm to move. However, in case the leader gets isolated due to similar reasons for a regular robot, the server can request all other robots to move to the leader and maintain the swarm connectivity; and (3) swarm partition — in the worst case, swarm may be divided to multiple isolated partitions and cannot per form any meaningful task for quite some time.

If there is only one partition that includes all robots in the swarm, the server knows that the swarm is connected. Otherwise, the swarm identifies amajorpartitionthatincludesthemaximumnum ber of robots, get its center location, and requests robots in all other partitions to move toward that location.

Multi-Level Password Checking

The access levels are classified underlying the access levels of administrators to general, high, and highest, which are determined by the passwords provided. Then, we restrict their permissions as follows: (1) general — under thisaccesslevel,the software can only monitor the basic information of theswarm and cannot issue control or coordination commands to any robot; (2) high — under this access level, the software can mon itor and coordinate the robot swarm, and watch the image/video from each individual robot, if applicable; and (3) highest — under this access level, the software can per form all functionalities including robot control.

With this multi-level password checking, we can avoid unauthorized interruption to the swarm. This policy can be changed according to the application and user requirements.

Server Information Reporting

To further enforce security, it is desirable to prevent those hackers who are not trying to control the swarm, but simply monitor robots positions. To address this potential vulnerability, each server running the software will require every connected robot to report all the servers it is communicating with. Then, authorized users can identify possible intruders by checking the reported servers.

Graphylene-Based Nanoribbons for Novel Molecular Electronic Devices

In the last decade, graphene has been frequently cited as one of the most promising materials for nanoelectronics. However, despite its outstanding mechanical and electronic properties, its use in the production of real nanoelectronic devices usually imposes some practical difficulties. This happens mainly due to the fact that, in its pristine form, graphene is a gapless material. We investigate theoretically the possibility of obtaining rectifying nanodevices using another carbon based two dimensional material, namely the graphenylene. This material has the advantage of being an intrinsic semiconductor, posing as a promising material for nanoelectronics. By doping graphenylene, one could obtain 2-dimensional p–n junctions, which can be useful for the construction of low dimensional electronic devices. We propose 2-dimensional diodes in which a clear rectification effect was demonstrated, with a conducting threshold of approximately 1.5 eV in direct bias and current blocking with opposite bias. During these investigations were found specific configurations that could result in devices with Zener-like behavior. Also, one unexpected effect was identified, which was the existence of transmission dips in electronic conductance plots. This result is discussed as a related feature to what was found in graphene nanoribbon systems under external magnetic fields, even though the external field was not a necessary ingredient to obtain such effect in the present case.

The unusual and remarkable mechanical and electronic pro perties of nanostructured carbon materials have attracted great attention from the materials science community in the last few decades. Of special relevance in this context is graphene, a two-dimensional material composed only by sp bonded carbon atoms forming a hexagonal pattern. On top of its well known impressive mechanical and electronic properties, this material presents a high chemical stability. There are, however, some drawbacks to the use of this kind of material in its pristine form, due to the fact that it is a semi-metallic material (or a gapless semiconductor). To overcome these problems, several different strategies have been attempted in order to create modified materials with small band gaps, relevant examples are oxidation, hydrogenation, fluorination or other modifications. It was also theoretically predicted, for a zigzag graphene nanoribbon, the possibility of a finite pseudogap in the band structure under a finite transverse magnetic field. With increas ing magnetic field, the pseudogap evolves towards the usual Zeeman splitting of a spin-degenerate band while the contribution to transmission falls down to half of the first plateau value. This was reported for a graphene point contact in the quantum Hall regime, where propagating modes progress from magnetoelectric sub-bands interacting with both edges to chiral edge states.

It is known as graphenylene or biphenylene carbon (BPC), a highly porous two-dimensional material, whose geometrical structure is based on the architecture of graphene, but with a different repeating unit, the biphenylene. The geometrical structure of graphenylene, despite being made only with carbon atoms andconstructed using the same architecture as graphene, presents important differences in comparison to its predecessor. One major feature is the presence of regularly spaced 12-membered pores, which are also organized as a hexagonal lattice. Other repeating geometrical motifs are hexagonal and square rings, which can bring about mixing between almost resonant and non-resonant chemical behavior for the structure. The unusual geometry of this material motivates several theoretical studies which explore possible modifica tions and applications such as gas separation,hydrogen purification, desalination lithium storage, catalysis, clean energy storage,etc. The topology obtained by combin ing periodically different geometrical motifs brings interesting electronic effects, such as the avoidance of a Dirac cone and opening of the band gap, turning the material into an intrinsic semiconductor. Its band gap value is similar to that found for silicon or germanium (~0.8 eV) and presents delocalized frontier orbitals. These characteristics pose graphenylene as a promising candidate to be used in semiconducting nano devices in the near future and motivated a few investigations for its practical use, including excitonic effects and tunning specific features of the electronic structure, such as the band gap opening. It is important to clarify that despite all the cited promising characteristics, graphenylene synthesis is quite recent and experimental methods for growing specific format samples, like nanoribbons, nanotubes or nanoscrolls, for instance, remain to be explored. In the present article, we investigate the electronic properties of nanodevices that could be constructed using graphenylene nanoribbons, either in their pristine or doped forms. The aim of this study is to explore the possibility of using 2-dimensional materials in the construction of nanodevices based on pn-junctions, similarly to what is done in traditional 3-dimensional semiconductor physics. Our results indicate that in the proposed junctions, several important physical phenomena occur, specially at the interface between the region doped with electron donor atoms and the region of electron acceptor atoms doping. Junctions are of fundamental importance in the construction of two key components for the vast majority of known electronic devices, namely the diode and the transistor. In order to study the proposed nanodevices, our investigations included the possi bility of obtaining an electric current rectifier. Another effect investigated was the partial blocking and unblocking of current by applying an electric field that could lead to the so called field effect transistor (FET). Recently, it was experimentally demonstrated that the presence of transmission dips in voltage gated semiconductor nanostructures, as InAs nanowire quan tum point contacts with and without an external magnetic field. Such an effect is related to the presence of helical states in the system, and its validation in the conduction band was done by comparing Zeeman pseudogap energy with the spin orbit energy. Actually, a hallmark to verify helical states is the so-called re-entrant behavior, which shows up as a mea surable dip in the conductance when an external magnetic field is strictly different from zero. Astonishing was the persistence of the dip at zero magnetic field. This zero-field pseudogap is justified as a result of an interplay between Coulomb inter action and the breaking of axial spin symmetry. This could be induced by the combination of a strong Rashba spin–orbit coupling (SO) and quantum confinement, under a spin flipping two-particle backscattering framework. It is presented as a promising way to experimentally study the existence and nature of Majorana zero modes in solid state systems, since those helical bound states are a prerequisite. More recently, helical hole states in Ge/Si core/shell nanowires were detected.42 The relevance of this discussion to the present work relies in the fact that we have found, without the application of any external field, electronic transmission dips, similar to those explained above. This result seems to be related to the presence of chiral states on graphenylene, caused by its unique topology.

How Channel Conditions Shape MIMO Performance in Land-Based Drone Operations

In recent years, drones have evolved from basic remote-controlled aerial devices into highly sophisticated land, sea, and airborne agents for data acquisition, defense, agriculture, and infrastructure inspection. A major leap in this evolution is the integration of Multiple Input Multiple Output (MIMO) communication systems. MIMO significantly enhances communication robustness and bandwidth efficiency by using multiple antennas to transmit and receive signals simultaneously. However, the true effectiveness of MIMO technology in land-based drone operations is intricately tied to the quality and behavior of the wireless communication channels it operates in.

Traditional single-input single-output (SISO) systems are constrained in terms of speed, interference resistance, and coverage—especially in cluttered or obstructed terrains. MIMO breaks through these limitations by leveraging spatial multiplexing, allowing the same frequency band to carry multiple data streams via separate transmit and receive antennas.

In the context of drones—particularly land-based autonomous systems—MIMO communication provides: (1) increased data throughput — essential for real-time video, telemetry, and sensor data transmission; (2) improved reliability — by exploiting multiple independent paths through multipath propagation; and (3) adaptive signal quality — by dynamically adjusting to changing environmental conditions and drone orientations.

Channel conditions refer to the set of environmental and electromagnetic characteristics that influence wireless signal propagation. For land-based drones, common influencing factors include: (1) multipath fasinf — as signals bounce off buildings, trees, and terrain, they arrive at the receiver at different times and phases, causing constructive or destructive interference; (2) path loss — signal attenuation increases with distance and obstructions between transmitter and receiver; (3) shadowing — large objects, such as hills or buildings, can block direct signal paths; and (4) doppler shift — movement of drones or surrounding objects causes frequency shifts that impact reception; these variables can drastically affect the signal-to-noise ratio (SNR), latency, and data integrity of drone communications.

To combat unpredictable channel behaviors, engineers have implemented triple-frequency redundancy modulation (TFRM)—a strategy that transmits redundant data across three separate frequency bands simultaneously. This not only provides diversity in spectral usage but also allows the system to: (1) fall back on a cleaner band if one becomes unusable; (2) exploit frequency diversity to enhance the likelihood that at least one copy of the transmitted data arrives error-free; and (3) provide faster re-synchronization in the event of a temporary link loss.

TFRM works hand-in-hand with MIMO, acting as a safeguard when channel conditions deteriorate due to localized interference, metallic surfaces, or transient atmospheric changes. In urban canyons or wooded terrain, where signal reflection and absorption are intense, this redundancy becomes essential.

Drones using MIMO and TFRM systems also benefit from adaptive modulation schemes. These allow the communication module to adjust its modulation strategy based on real-time measurements of channel quality. For example: (1) high SNR conditions — use 64-QAM (Quadrature Amplitude Modulation) for high data rates; (2) moderate SNR conditions — drop to 16-QAM for a balance between speed and robustness; and (3) low SNR conditions — default to BPSK (Binary Phase Shift Keying) for maximum resilience.

This flexibility ensures communication continues even in the presence of severe signal degradation—critical during reconnaissance missions or while navigating inside structures where GPS and direct line-of-sight radio signals may be compromised.