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Survey of Important Issues in UAV Communication Networks

Lav Gupta, Raj Jain, Gabor Vaszkun

arXiv:1603.08462v1cs.NI

TL;DR

UAV networks offer valuable multi-UAV communication capabilities, but their mobility, intermittent links, failures, energy constraints, and partitioning expose gaps in existing MANET and VANET approaches. This survey synthesizes research on UAV-network characterization, routing, SDN-enabled control, seamless handover, disruption tolerance, and energy efficiency. It concludes that UAV networks require specialized protocols and architectures, while several areas remain insufficiently explored.

  • Problem

    UAV networks have distinctive mobility, topology, link, power, and reliability conditions that existing MANET and VANET research does not fully address.

  • Method

    The paper surveys and categorizes research on UAV-network characteristics, routing, handover, disruption tolerance, and energy efficiency.

  • Results

    The survey concludes that UAV routing requires support for dynamic topology, node changes, intermittent links, bandwidth and energy constraints, reliability, latency, mobility, and QoS.

  • Takeaways & Limitations

    Delay-tolerant store-carry-forward designs are considered effective when UAV networks are partitioned and continuous end-to-end connectivity cannot be assumed.

Abstract

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Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications where human lives would otherwise be endangered. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network leads to the requirement of seamless handovers where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute towards greening of the network. This article surveys the work done towards all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.

I. INTRODUCTION

UAV networks offer important public and civil applications, but their fluid topology, intermittent links, mobility, failures, energy limits, and partitioning create challenges that conventional ad hoc networking does not fully address. The survey therefore focuses on architectural, routing, disruption-tolerance, SDN, handover, and energy-efficiency issues.

  • UAVs support public and civil applications including disaster response, rescue, medical delivery, and environmental monitoring.
  • UAV networks have fluid topologies, intermittent links, varying mobility, node failures, link disruptions, and high error rates.
  • Energy constraints, limited flight time, and frequent network partitioning require energy-saving mechanisms and delay- and disruption-tolerant communication.
  • The survey addresses understudied UAV-network issues including self-organization, disruption tolerance, SDN control, seamless handover, and energy efficiency.

II. CHARACTERIZING THE UAV NETWORK

UAV communication networks differ from MANETs and VANETs in mobility, topology, node roles, architecture, and disruption behavior. These differences make architecture selection and reliable routing central design problems.

  • Multi-UAV systems provide redundancy and survivability, but their protocols must handle changing topology, mobility, power constraints, and node failures.
  • UAV networks may include stationary, slow-moving, or highly mobile nodes and can require infrastructure-based or ad hoc architectures.
  • UAV nodes commonly act as servers that route client traffic or relay sensor data to control centers.
  • Star architectures can impose blockage, higher latency, bandwidth demands, and dependence on ground nodes, whereas mesh architectures support multihop communication.
  • Routing must address dynamic three-dimensional topology, node addition and removal, intermittent links, bandwidth and energy constraints, reliability, latency, mobility, and QoS.

C. Categorization of UAV Networks

The survey categorizes UAV networks according to application-driven properties such as mobility, architecture, routing, control, and service role. It also examines mesh self-organization as a means of maintaining communication despite failures.

  • UAV-network categorization begins with application requirements and derives the mobility, architecture, routing, and control properties needed to support them.
  • UAVs may hover as communication infrastructure providing area-wide voice, video, and data coverage.
  • In infrastructure applications, UAVs can operate under ground-control supervision or form a mesh while serving as communication and control-data relays.
  • Mesh networks can self-form, reorganize after node failures, and provide inherent fault tolerance.
  • Self-organization remains challenging because reconfiguration requires monitoring, message exchange, computational overhead, and bandwidth.

E. SDN-automating UAV network control

UAV networks face intermittent connectivity, impaired channels, dynamic nodes, and changing topologies that complicate resource use and interoperability. SDN addresses these conditions by separating programmable control from forwarding and supporting centralized or distributed network management, although its benefits and adaptation to infrastructure-less wireless networks remain insufficiently understood.

  • Motivation: Intermittent connectivity, impaired channels, and limited communication resources complicate UAV network planning and resource allocation.Different higher-layer protocols can also prevent nodes using the same access technology from operating across networks.
  • SDN capabilities: SDN enables UAVs to define protocol stacks in software and adapt flexibly to different environments.It also supports programmable deployment and management of applications, services, network policies, and performance.
  • Open issues: SDN deployment has been extensive in fixed infrastructure, but its advantages and appropriate design for infrastructure-less wireless networks remain unclear.Open issues include control balance, controller cooperation, security, and flexibility in dynamic mobile environments.
  • SDN capabilities: OpenFlow separates forwarding and control functions through programmable switches, flow tables, and controller communication.This separation enables finer control, automated operations, and resource optimization in dynamic environments.
  • Control architectures: SDN control may be centralized, distributed across UAVs, or hybrid, with controllers maintaining network views and updating switch flow tables.Centralized control may reside on the ground or airborne, whereas distributed control assigns packet processing to local agents and exchanges control traffic among SDN elements.
  • UAV-specific requirements: UAV networks require protocols that accommodate variable mobility, node failures, power constraints, intermittent links, and delay or disruption.The surveyed routing work includes traditional protocols, variants, and UAV-specific approaches for these conditions.

A. Routing issues to be resolved

UAV routing must address location and energy awareness, intermittent links, changing topology, mobility, power constraints, and QoS requirements beyond conventional ad hoc routing. The survey reviews static, proactive, reactive, hybrid, geographic, hierarchical, and 3D routing approaches, including protocols designed for dynamic airborne networks.

  • Routing requirements: UAV routing requires location-awareness, energy-awareness, robustness to intermittent links, and adaptation to changing topology in addition to generic mesh-network requirements.The paper identifies network-layer design for UAV networks as a major challenge.
  • Routing requirements: Existing MANET protocols may be unsuitable for airborne nodes because UAV requirements vary in dynamicity and link interruptions.The paper therefore calls for protocols tailored to high mobility, dynamic topology, and different routing conditions.
  • Routing classifications: Static routing tables cannot be updated during operation, making static protocols unsuitable for dynamically changing UAV environments and limiting their applicability.The survey presents static routing mainly for academic interest.
  • Routing classifications: Proactive protocols maintain current routes but require repeated updates, creating bandwidth, computation, and storage burdens for UAV networks.DSDV illustrates these costs through periodic updates despite incremental-dump mechanisms; BABEL limits event frequency and supports link-quality estimation.
  • Proposed and specialized protocols: RGR combines UAV location information with reactive end-to-end paths and outperforms AODV in search missions for delay and packet delivery ratio.The survey also reviews hierarchical, geographic, and 3D schemes, including GDSTR-3D and MDT, which target delivery guarantees and low routing stretch.
  • Proposed and specialized protocols: Hierarchical clustering can limit duplicate AODV route requests to selected clusters, while ZRP may increase complexity and communication congestion between subnets.ZRP efficiency is strongly influenced by zone radius, and added complexity may outweigh modest performance gains.

C. Routing in networks prone to delays and disruptions

UAV networks can remain partitioned and disconnected long enough for conventional end-to-end routing and TCP assumptions to fail. The survey therefore examines delay-tolerant approaches based on deterministic knowledge, stochastic contacts, buffering, and store-carry-forward delivery.

  • Motivation: Intermittent connectivity and long-lasting partitions cause delays beyond TCP thresholds, leading to dropped packets and inefficient TCP transmission.Traditional routing protocols may simply discard packets when no end-to-end path exists.
  • Delay-tolerant architecture: Delay and disruption tolerance requires store-carry-forward operation, in which nodes buffer data until forwarding opportunities arise.End-to-end request-response, acknowledgements, and timed-out transmission are unsuitable under long delays and disconnections.
  • Routing approaches: The survey classifies delay-tolerant routing into deterministic, stochastic, and social-network approaches, with stochastic methods including epidemic, estimation, movement-control, and coding-based schemes.The paper also discusses direct delivery, geographical routing, and ant-colony-based exploration and exploitation.
  • Deterministic routing: Deterministic routing uses known future movement and links, providing single-copy unicast with lower resource usage when UAV trajectories are coordinated or predictable.Its representation uses time-varying link capacities between delay-tolerant nodes.
  • Stochastic routing: Stochastic routing addresses random, unknown network behavior by choosing forwarding opportunities using contacts, historical data, mobility patterns, or related information.These protocols maintain a time-varying topology updated when nodes encounter one another.
  • Stochastic routing: Epidemic routing replicates messages across relays and can provide low delay and high success rates when buffers are large, contacts are long, or network load is low.Without movement information, nodes forward packets to some or all neighbors except the previous sender.

IV. SEAMLESS HANDOVER

Seamless handover is a key issue for UAV networks because nodes can move, fail, or leave service during missions. The supplied passages identify delay/disruption-prone routing as a related concern but do not provide detailed handover mechanisms here.

  • Handover motivation: UAV nodes may operate at different mobility levels, periodically leave service because of depleted power or faults, and shut down interfaces to conserve energy.These conditions make continuity of communication a central network concern.
  • Handover motivation: Delay/disruption-prone UAV networks are identified as a distinct routing context requiring protocols that tolerate intermittent connectivity and partitioning.The supplied material includes a table specifically titled “Delay/Disruption Prone UAV Networks DTN Routing.”

A. Handoffs in UAV networks

Handover preserves service as a mobile device moves between access points or networks. UAV research considers hard, soft, horizontal, vertical, and hybrid handovers, but hybrid handover work remains limited in UAV networks.

  • Handover types: Hard handover breaks the old connection before establishing the new one, whereas soft handover establishes the new connection first and preserves sessions until migration.The distinction is illustrated in Figure 5 through hard and soft handover cases.
  • Handover types: Horizontal handover transfers a device between access points in the same network, while vertical handover transfers it between networks using different technologies.Figure 6 depicts the horizontal and vertical categories.
  • Handover control: Hybrid handover involves both the mobile device and the network, but relatively little work has studied it in UAV networks.The paper notes that hybrid handover has been studied in VANETs and mobile IP networks.

B. Applicability of Existing Handover Schemes

Existing MANET and VANET handover approaches provide partial guidance for UAV networks, but high mobility, topology changes, partitioning, and latency leave seamless handover unresolved.

  • Applicability of existing work: Studies of UAV handover remain limited, so researchers have examined mobility protocols developed for MANETs, VANETs, and wireless mesh networks.The paper notes limited efficient seamless-handover studies even in IEEE 802.11-based WMNs and VANETs.
  • Handover challenges: Frequent topology changes and network partitioning can invalidate routes, trigger handovers, and cause latency and packet-loss degradation.These conditions are associated with highly dynamic vehicular and UAV environments.
  • Existing schemes: PMIPv6 with a mobility manager performs better than MIPv6 overall, especially with IEEE 802.11p.The manager monitors and selects the best access technology and network.
  • Existing schemes: The PMIPv6-based approach can hand over between two IEEE 802.11p networks without data loss at moderate speeds.MIPv6’s major problem is handover latency before the node can resume packet transmission or reception.
  • Mobility scope: Traditional MIPv4, MIPv6, and HMIPv6 support terminal mobility rather than network mobility, motivating consideration of NEMO for UAV scenarios.The UAV handover procedure transfers physical-layer connectivity and state information between UAV nodes for the relevant mobile unit.

C. Media Independent Handover

Media Independent Handover provides a technology-independent interface and link-layer information for heterogeneous-network handovers, while leaving several policies and technologies outside its scope.

  • MIH architecture: IEEE 802.21’s Media Independent Handover Function connects layer-2 wireless technologies with IP at layer 3.MIHF supports handover and interoperability across wired and wireless IEEE-802 media.
  • MIH operation: MIH combines mobile-station measurements and network information to supply signaling and triggers for handover optimization.Measurement reports can initiate handover using link-layer information such as available-network signal-to-noise ratios.
  • Scope and limitations: MIH does not provide intra-technology handover, handover policies, security, or link-layer-specific enhancements.Those functions remain the responsibility of the respective link-layer technologies.
  • Emergency interoperability: MIH can provide a common interface for heterogeneous multi-operator networks during cross-jurisdiction emergency response.The interface uses technology-independent primitives for higher layers.
  • Applications: Using MIH, UAV networks can combine live video feeds with WiFi, WiMAX, LTE, and other networks for search-and-rescue or troop-assistance applications.The cited applications involve live assistance and real-time information delivery.
  • Scope and limitations: MIH remains nascent and requires implementation by both mobile devices and the network before deployment can work.The passage states that it has not been widely deployed and evaluated.

V. ENERGY EFFICIENCY IN UAV NETWORKS

Communication equipment substantially reduces UAV endurance because it consumes energy even while idle, creating trade-offs between flight time, network lifetime, and payload.

  • Energy burden: 1280mA/940mA/820mA/100mA are typical WiFi 802.11n interface currents in transmission/reception/idle/sleep modes.The idle draw shows that energy consumption persists without transmission or reception.
  • Energy burden: 16% is the estimated reduction in rated flight time when mesh communication equipment shares a small drone’s battery.GPS and sensors can raise the total reduction beyond 20%.
  • Battery configurations: Separate communication batteries shift the constraint from shared energy use to payload capacity.The paper describes separate batteries powering an airborne OpenMesh router.
  • Battery configurations: 30% of available payload was occupied by eight 92g batteries in the reported experiment.The UAV had approximately 1kg dead weight and 300g payload capacity.
  • Energy-saving rationale: Reducing communication energy can increase network lifetime or the useful payload carried by the UAV.The stated benefit follows from lowering the battery burden.
  • Scope: Energy harvesting is an evolving specialized area and was excluded from this survey.The passage identifies kinetic, thermal, and electromagnetic sources as harvesting possibilities.

A. Energy Conservation in the Network Layer

Network-layer energy conservation in UAV networks spans power-aware routing, node selection, coordination, and sleep strategies, but protocol suitability across scenarios remains unsettled.

  • Motivation and taxonomy: The applicability of most MANET-derived routing protocols to different UAV scenarios remains unproven, with no clear best protocol class.Simulation results favor different protocols in different cases.
  • Motivation and taxonomy: Energy conservation mechanisms measure or control power, distribute load, put nodes to sleep, or select low-energy paths.The survey classifies protocols into path-selection, node-selection, coordination, and sleep-based categories.
  • Path-selection protocols: EMM-DSR extends DSR to maximize energy efficiency while maintaining end-to-end delay and throughput performance.It uses an energy-based shortest-path mechanism and extends Max-Min routing objectives.
  • Path-selection protocols: FAR minimizes link-cost sums but assumes a static network, making frequent UAV movement computationally expensive.Its repeated minimum-cost route computations incur penalties when node positions change often.
  • Path-selection protocols: Minimum-energy multi-hop routing can split high-power hops into lower-power hops and use Dijkstra’s algorithm to select the lowest-energy path.An ExOR-like scheme prioritizes candidate forwarders by closeness to the destination and forwards unacknowledged packets opportunistically.
  • Node and coordinator selection: DEAR uses existing RREQ packets to estimate network battery information without extra control packets and selects relatively high-energy routes.Simulation reportedly shows longer network lifetime and improved delivery ratio through more reliable paths.
  • Node and coordinator selection: GAF and related coordinator schemes save energy by letting selected nodes remain active while others sleep, but UAV mobility can destabilize coordinators and partition networks.Frequent candidate changes and low-energy grid areas are identified as UAV-specific concerns.
  • Sleep-based protocols: CDS keeps a connected dominating set active while other nodes sleep, allowing routing to adapt to topology changes and conserve energy.CDS members are selected using energy metrics.

B. Energy Conservation in the Data Link Layer

Data-link-layer energy conservation in UAV networks uses sleep scheduling, signaling, topology control, and clustering, while balancing energy savings against delay, throughput, connectivity, and fairness.

  • Duty-cycle protocols: MAC protocols reduce energy by scheduling sleep and active periods, using either a single radio or separate signaling and data radios.Power-saving modes reduce idle consumption, but waking devices for packet exchange can increase delivery delay.
  • Duty-cycle protocols: S-MAC uses fixed sleep cycles, whereas T-MAC adapts schedules after inactivity to improve efficiency over fixed scheduling.S-MAC can waste energy under low traffic and reduce throughput.
  • Duty-cycle protocols: STEM, PTW, LEEM, and PAMAS use signaling, pipelined wakeups, synchronization, or radio switching to manage energy-delay tradeoffs.STEM incurs wakeup delay; PTW shortens wakeup time, LEEM sequentially wakes nodes along a path, and PAMAS switches off radios when the medium is unavailable.
  • Topology and power control: Topology and power-control protocols select links or adjust transmission power to reduce energy use while addressing connectivity, collisions, and hidden terminals.XTC ranks neighbors by link quality, LFTC adjusts power and improves over XTC in hop count and data delivery, while PEM schedules transmissions using interference relations.
  • Cluster-based MAC protocols: Cluster-based MAC protocols aggregate traffic through coordinators, allowing non-cluster nodes to follow power-saving sleep schedules.LEACH forms clusters around cluster heads, while PEGASIS forms a chain of cluster heads and selects one for aggregation.

C. Energy conservation in the physical layer

Physical-layer energy conservation addresses UAV mobility and three-dimensional communication through voltage control, topology and node power management, and sleep-oriented mechanisms.

  • Physical-layer challenges: High UAV mobility and three-dimensional movement make physical-layer design more involved because communication distances and link conditions vary.The physical layer must support connectivity, encoding, signaling, and bit transmission under changing aerial-network conditions.
  • Device-level power management: Dynamic Voltage Scaling lowers bias voltage under low traffic to reduce circuit energy use while trading energy quality against workload and latency constraints.Lower-voltage fallback can increase UAV flying time as battery voltage drops.
  • Topology and node power control: Topology-control methods vary transmission power or select active nodes to maintain connectivity, improve network lifetime, and reduce energy consumption.LM-SPT uses one-hop neighborhood information, while minimum-energy topology limits power to coverage of nearest neighbors.
  • Topology and node power control: Node and network power-management schemes switch off idle processors, wireless interfaces, or base stations while considering coverage, traffic routing, capacity, and service demands.CPLDs reduce standby power, Virtual WLAN consolidates hardware, and time-variable mesh control selects active base stations dynamically.
  • Sleep and rate adaptation: Sleep-oriented schemes buffer packets during inactive periods so interfaces can sleep longer, but buffering may increase delay and amplify bursts.SRA combines short idle-period sleep with buffering for sleeping clients.

D. Energy Conservation through Cross-Layer Protocols

Cross-layer energy conservation is needed because optimizing individual UAV-network protocol layers can produce inefficient overall energy use, yet such protocols remain understudied.

  • Cross-layer motivation: Network, data-link, and physical layers address different energy-related functions, including path selection, collision avoidance, duty cycles, voltage control, and power control.These layer-specific responsibilities make energy conservation a distributed protocol concern.
  • Cross-layer motivation: Optimizing energy conservation in one protocol layer may still result in inefficient energy conservation across the entire network.The paper identifies this mismatch as a motivation for cross-layer protocol research.
  • Cross-layer research gap: Very little work is available on cross-layer protocols in mobile ad hoc networks and their application to UAV networks.The passage frames cross-layer design as an open research area rather than a settled solution.

VI. CONCLUSIONS

UAV networks require specialized approaches because mobility, fluid topology, intermittent links, limited energy, failures, and partitions challenge conventional networking. The survey organizes open issues around network characterization, routing, disruption tolerance, seamless handover, SDN-enabled control, and energy efficiency.

  • Network characteristics: UAV networks differ from conventional ad hoc networks through dynamic topology, intermittent links, mobility, bandwidth constraints, and limited power.These characteristics motivate designs spanning multiple protocol layers.
  • Network characterization: The survey classifies UAV networks by architecture, application role, topology, and delay- or disruption tolerance before examining their principal communication challenges.The classification distinguishes infrastructure and ad hoc, server and client, and star and mesh deployments.
  • Routing: Routing protocols must accommodate mobility, changing topology, node additions and removals, intermittent links, bandwidth limits, energy constraints, latency, reliability, and quality of service.Static and proactive approaches have limited suitability, while reactive and hybrid protocols involve different delay, overhead, and complexity trade-offs.
  • Disruption tolerance: Delay- and disruption-tolerant networking is needed when partitions make continuous end-to-end connectivity unavailable.Store-carry-forward designs select carriers according to their delivery probability, while flooding consumes substantial buffer, bandwidth, and power.
  • Seamless handover: Seamless handover seeks communication continuity with only a minor latency increase, but handover latency and packet loss can degrade performance and quality of service.IEEE 802.21 MIH supports interoperability but lacks several handover functions and remains insufficiently deployed and evaluated.
  • Energy efficiency: Energy efficiency can extend network lifetime and increase useful payload through transmission-power control, load distribution, sleep scheduling, and cross-layer mechanisms.The survey identifies contributions from the physical, network, and data-link layers.
  • Research agenda: The survey targets understudied UAV-network issues and presents open research directions intended to support stable, reliable, context-specific multi-UAV networks.Its focus includes characterization, routing, SDN-based control, seamless handover, disruption tolerance, and greening.
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