Service function chain (SFC) deployment is to embed virtualized network function (VNF) instances into a cloud-based infrastructure and chain them in sequence to provide a specific network service. Many approaches have been proposed for SFC deployments with diverse objectives. However, no SFC deployment approach factors in the embodied and operational carbon emissions, which are complicated by location- and time-varying carbon intensity (CI) and the amortization of carbon footprint among SFCs when they share VNF instances and communication links. Moreover, prioritizing lower carbon emissions for an SFC might degrade its end-to-end latency and vice versa. This work aims to jointly minimize carbon emissions and latency in SFC deployment, factoring in 1) SFC lifetime, traffic rates, and resource usage, 2) amortized embodied and operational carbon emissions, and 3) processing and propagation delay. We propose a rule to amortize the carbon footprint to SFCs and an SFC deployment algorithm based on Monte Carlo Tree Search (MCTS) with a time-series forecasting method to predict spatial and temporal fluctuations of CIs. Simulations based on real-world historical CI data and dynamic SFC arrivals and departures confirm the effectiveness of the proposed approach.

To support the 5G Wireless and Wireline Conver-gence (5G-WWC) architecture, the Access Gateway Function (AGF) is defined to serve as an interworking function between the 5G core (5GC) and fixed network residential gateways (FN-RG) with a non-3GPP wireline access network. AGF acts as a proxy to communicate with 5GC on behalf of FN-RG. This paper presents a design for AGF that realizes control and user plane separation. Three different implementations based on user-space, kernel-space, and kernel-bypass (DPDK) softwares are reported and tested. The results confirm the superiority of the kernel-bypass approach in terms of latency and throughput of user-plane traffic.

A user (model owner) in federated learning builds a learning model by aggregating local learning models trained by independent workers with their private datasets. A fundamental issue of federating learning is allocating resource from workers to the training task. As the allocation causes extra costs and overheads, workers are inherently reluctant to participate. Therefore, it is crucial to design an incentive-based resource allocation mechanism (incentive mechanism) that motivates workers to contribute their resources. Though some incentive mechanisms have been proposed for federating learning, none has devoted to the case when multiple users coexist and compete for worker service whereas a worker can contribute to multiple training tasks at the same time. For this scenario, this paper proposes an auction-based approach, where multiple users as buyers place bids for worker's service. We devise two algorithms attempting to find an auction result that maximizes social welfare, together with a pricing rule that ensures incentive compatibility and individual rationality. Simulation results show that one of the algorithms, which is based on the alternating direction method of multipliers (ADMM), outperforms the other greedy algorithm in terms of social welfare particularly when workers do not have adequate computing resource for all the training tasks.

Many studies have been devoted to channel allocation for backhaul links in wireless mesh networks. Among them, a game-theoretic approach proposed by Yen and Dai is promising for the ability to self-stabilize to a valid solution in a decentralized manner. However, game-based solutions are generally not optimal. Furthermore, Yen and Dai's approach did not fully utilize all available channels, wasting scarce bandwidth resource. In this paper, we propose two learning-based approaches to enhance the prior work. One uses Spatial Adaptive Play (SAP) for agents to learn best probability distributions on their possible channel selections. The other based on multi-agent reinforcement learning (MARL) algorithm allows each agent to find out its best selection over time. Simulation results reveal that the proposed approaches do improve the game-based solutions in terms of the number of operative links after channel allocations.

WMN's mesh access points (MAPs) are linked through a wireless backhaul network that consist of mesh points (MPs) that is equipped with multiple radios that use multiple non-overlapping channels in parallel. MPs will establish designated links that should satisfy both common channel constraint and interference constraint which are conflicting in nature. Yen and Dai proposed game-theoretic radio resources allocation in WMN which is better than centralized and greedy approach if only two radios are available at each node, but when there are more than two radios per node, centralized and greedy approach perform better. So, this study would like to utilize reinforcement learning to improve previous research so the approach is also effective if there are more than two radios available per node. This study attempts to maximize the number of operative designated links in the backhaul networks subject to common channel constraint and interference constraint. We use multi-agent deep Q-learning to tackle this problem. We conduct simulations to compare the proposed approach with game based approach. The results of our experiments show that the proposed deep Q-learning algorithm performs better than game-theoretic approach in dense network where there are more than two in each MP, while the game-theoretic approach performs better than our proposed DQL algorithm in sparse network.

Traffic splitting is to distribute traffic over multiple paths so as to better utilize link bandwidth and avoid potential link congestion. Many traffic splitting schemes have been proposed for datacenter networks, which provide different levels of splitting granularity. This paper proposes a packet-level traffic splitting scheme, which provides the most fine-grained link utilization as it lays out a forwarding path for each individual packet. The proposed scheme mitigates possible packet reordering problem by equalizing the build-ups of packet queues in all switches as the path layout utilizes links in proportional to the associated exploitable link bandwidth. The result is a packet dispatching rule designated to each switch. We implemented the rules using P4 (Programming Protocol-Independent Packet Processors) switches and conducted experiments to measure the performance. Experimental results show that the proposed scheme provides higher goodput compared with another packet-level traffic splitting scheme which dispatches packets randomly among all links.

In software defined networking (SDN), a controller may manage several SDN switches to be cost-effective while a switch may demand management service from multiple controllers for fault tolerance. The controller placement problem (CPP) is to determine the locations of SDN controllers to minimize the total deployment cost subject to constraints such as controller-switch latency, inter-controller latency, and controller capacity. This problem is challenging especially in interconnected geo-distributed SDN networks. Existing centralized solutions do not well adapt to network dynamics. This paper proposes several distributed mechanisms based on the exact potential game. These mechanisms dynamically adapt to network faults such as link and controller failures. The simulation result shows that these mechanisms need fewer controllers than an existing approach in static networks. When links or controllers may fail, our mechanisms still perform better while only a part of the network nodes is affected. The latter is impossible in non-adaptive approaches.

Network Function Virtualization (NFV) enables the embedding of Virtualized Network Function (VNF) into commodity servers. A sequence of VNFs can be chained in a particular order to form a service chain (SC). This paper considers placing multiple SCs in a geo-distributed edge system owned by multiple service providers (SPs). For a pair of SC and SP, minimizing the placement cost while meeting a latency constraint is formulated as an integer programming problem. As SC clients and SPs are self-interested, we study the matching between SCs and SPs that respects individual's interests yet maximizes social welfare. The proposed matching approach excludes any blocking individual and block pair which may jeopardize the stability of the result. Simulation results show that the proposed approach performs well in terms of social welfare but is suboptimal concerning the number of placed SCs.

A service chain (SC) comprises a series of service functions realized as virtualized network functions (VNFs). Each VNF has specific resource, bandwidth, and location requirements. This study assumes multiple SCs to be placed on geo-distributed heterogeneous edge servers owned by multiple edge service providers (ESPs). Each ESP selectively hosts SCs to earn profit. We consider the problem to maximize the total profit (i.e., social welfare) of all ESPs by organizing ESPs into a set of disjoint federations called federation structure. We formulate the problem of finding an optimal federation structure, identify the difficulties in solving the problem, and present two time-efficient heuristics as our approach. Simulation results show that elaborate federation structures do bring in higher social welfare compared with simple all-in or all-out ESP organization. The proposed heuristics can also approximate the optimal result in many cases.

In recent decades, unmanned aerial vehicles (UAVs) have been widely adopted such as serving as flying base stations. Compared to traditional solutions, the deployment of UAV s is fast and low-cost. However, due to UAVs' limited energy capacity, efficiently utilizing UAV s' energy is a matter of concern. One solution is to use rechargeable UAV s, but the challenge of charging schedule comes in the wake of it. In this paper, this problem is modeled as a non-cooperative game where a UAV can choose a strategy in order to maximize its payoff. Also, we have proved that the proposed game is an exact potential game (EPG) which ensures a Nash equilibrium (NE) with best-response dynamics. Numerical results show that the proposed algorithm has a larger coverage ratio and is more flexible than other algorithms in most environments; hence, a better performance.

Recent advance of Network Function Virtualization (NFV) enables deployment of NFV-based network services on a cloud-base infrastructure. Meanwhile, edge computing provides cloud infrastructure on the the edge of access network to serve end user. This paper proposes a decentralized market-based framework that matches independent network service providers (NSPs) with independent edge service providers (ESPs) for possible deployments of network services with associated payments. The framework benefits all participants by guaranteeing that the results are profitable for all participants: no NSP or ESP can be better off by dropping out of the match result. Simulation results indicate that the proposed approach had the highest average number of deployed network services compared with counterparts.

As the next generation network is supposed to support diverse service requirements, managing Quality of Service (QoS) is a crucial part of it. QoS guarantees have long been deemed too complicated until the emergence of software-defined networking (SDN) and widely adopted standard OpenFlow. Recently, Programming Protocol-independent Packet Processors (P4) has gained much attention because of its features like programmable data plane and independent protocol and platform. It is anticipated that the high flexibility of P4 can enhance the QoS control for production networks. In this paper, we show a design of bandwidth management for QoS with SDN and P4-programmable switch. The design classifies packets into different categories based on their QoS demands and usages, which are then disaggregated by a two-level priority queue. Experiments with P4 switch shows that the proposed design not only effectively limits the maximum allowed rate but also guarantees the minimum bandwidth of each traffic flow. As such, the design can maximize bandwidth utilization and serves a building block for network slicing.

Network function virtualization (NFV) has been a promising approach to flexible and scalable deployment of network services. In NFV management and orchestration (NFV-MANO) architectural framework, VNF managers (VNFMs) should be deployed to manage the lifecycle of virtualized network functions (VNFs). VNFM placement problem (MPP) is to deploy VNFMs that minimizes overall operational cost while meeting performance requirements. The only existing approach to MPP is centralized and does not well adapt to network dynamics (e.g., VNFM failures, ups and downs of VNF instances, etc.) We leverage game theory to achieve distributed solutions to the MPP, which are self-adaptive in the sense that each VNFM locally and autonomously adapts to network dynamics without a central control. Simulation results show the proposed approaches can adapt to network dynamics and have lower total cost than the counterpart in large-scale NFV systems.

Vehicular-fog system consists of vehicles with computing resources that are mostly under-utilized. Therefore, an edge system may offload some workloads for remote execution at nearby vehicular- fogs. Whether this is cost-effective depends on not only the costs and computation capacities of vehicles but also the amount of workloads and associated latency constraint. In this paper, we consider a two-tier federated Edge and Vehicular- Fog (EVF) architecture and aim to minimize overall cost while meeting latency constraint by setting up an appropriate offloading configuration. We model this to a singleobjective mixed integer programming problem. To solve this mixed integer problem in real time we propose an iterative greedy algorithm using the queuing model. The results show, our proposed architecture reduces the cost of vehicular-fogs by 40â€"45% and the total cost by 35â€"40% compared to the existing architecture and help the edge to provide services beyond its capacity with specified latency constraint.

Multi-access edge computing (MEC) system consisting of geographically-distributed heterogeneous servers can provide low-latency virtualized resource to support computation offloading of smart devices. When bulk offloading requests comes to an MEC system, how to dispatch requests to servers so as to maximize divergent objectives of MEC service providers and users is challenging. The problem further involves money transfer when different MEC service providers can share resource to each other. In this paper, we address request dispatch issues in a standalone MEC and among federated MEC systems using matching game theory. We have adapted several classical matching algorithms to our problem. Simulation results show that we can serve more requests while still meeting latency constraints. For federated MEC systems, we can also have high revenue.

Software-defined networking (SDN) has been a promising solution to multicast streaming data due to its scalability and manageability. However, offering a multicast service that spans a large geographical area is still challenging because we still lack a unified multicast scheme that interconnects independent SDN-managed networks and bridges the service between SDN- based networks and the rest of the Internet. This paper proposes a solution that integrates SDN, CORD, and PIM technologies. We provides preliminary performance evaluation results. This work serves as a stepping stone to the ultimate goal of multicast as a service.

All active sessions of an ordinary host will be broken if the host changes its IP address as a result of migrating to a new subnet. Traditional solutions toward this problem either need modifying mobile hosts or create tunnels that cause inefficient triangle routing. SDN-based mobility schemes, on the other hand, focus on handover latency reduction or fast packet redirection after handover. There is no SDN-based approach that handles autonomous IP address changes by mobile hosts. As a remedy, we propose SDN-enabled Session Continuity (SDN-SC), as an SDN-based mobility management mechanism that retains session for hosts roaming across subnets in an SDN network. Particularly, SDN-SC suppresses possible address re-configurations with techniques such as gateway spoofing and DHCP lease renewal for mobile hosts away from home. We studied the performance of SDN-SC and compared it with that of MIPv4 and PMIPv6. The results show that SDN-SC outperforms both MIPv4 and PMIPv6 in terms of handover latency.

Unmanned aerial vehicle (UAV) nowadays are inexpensive and can serve as a robust communication platform in the sky. Using UAV as access point can be particularly helpful for wireless terminals in areas where terrestrial infrastructure is absent or damaged. The problem is how to deploy a fleet of UAVs to efficiently cover scattered terminals while not wasting too much energy on the deployment process. In this paper, we propose two autonomous service deployment approaches for UAVs based on game theory. In these approaches, UAVs adaptively adjust their locations based on local information rather than instructions from a control station. Experimental results show that the proposed approaches outperform existing approach in terms of average spectral efficiency and UAV travelling distance.

Mobile devices equipped with multiple network interfaces have the potential to increase transmission throughput by exploiting concurrent multipath transmission (CMT). However, packet scheduling for CMT is challenging since the diversity of latencies among transmission paths can easily yield out-of-order packet receptions and cause receiver buffer blocking if the receiver buffer is not large enough. Previous studies proposed several solutions attempting to eliminate out-of-order receptions and receiver buffer blocking. Delay-Aware Packet scheduling (DAPS) is the only one among them that considers delay diversity at the sender side when scheduling packets. However, DAPS assumes quasi-static path delay and thus does not perform well if path delay changes dynamically. In this paper, we analyze how jitter affects the performance of DAPS. Furthermore, we propose and implement a jitter-aware packet scheduler named JAPS. Performance evaluations show that JAPS outperforms existing algorithms in terms of throughput under different settings of data volume, receiver buffer size, network jitter and bandwidth ratio.

Low-latency handover is crucial to real-time traffic in wireless networks. This paper considers an enterprise network managed by emerging Software Defined Network (SDN) technology. For this networking environment, we propose a mobility management scheme named Mobility SDN (M-SDN) that reduces the traffic pause time caused by a host-initiated layer-2 handover. M-SDN performs handover preparation in parallel with the layer-2 handover that involves N-casting of active flows to every potential handover target. Handover preparation is enabled by efficient address resolution and location tracking. We have implemented a prototype of M-SDN and conducted several experiments to evaluate the performance of M-SDN. Experimental results show that M-SDN effectively reduces the impact of layer-2 handovers without any modification on mobile devices.

Self-stabilizing systems tolerate transient faults by always returning to a legitimate system state within a finite time. This goal is challenged by several system features such as arbitrary system states after faults, various process execution models, and constrained process communication means. This work designs self-stabilizing distributed algorithms from the perspective of game theory, achieving an intended system goal through private goals of processes. We propose a generic game design for identifying a maximal independent set (MIS) or a maximal weighted independent set (MWIS) among all processes in a distributed system. From the generic game several specific games can be defined which differ in whether and how neighboring players influence each other. Turning the game designs into self-stabilizing algorithms, we obtain the first algorithms for the MWIS problem and also the first self-stabilizing MIS algorithm that considers node degree (including an analysis of its performance ratio). We also show how to handle simultaneous moves of processes in some process execution models. Simulation results indicate that, for various representative network topologies, the new algorithm outperforms existing methods in terms of MIS size and convergence rate. For the MWIS problem, the new algorithms performed only slightly worse than centralized greedy counterparts.

Sniffer tools capture protocol data. Kernel-profiling tools track function calls and events occurring in the kernel. These two types of tools help us observe external and internal behaviors of networking protocols, respectively. We need both types of data for a comprehensive view of protocol behavior. However, none of existing tools performs these two tasks in an integrated way. We developed Kernel and Protocol Analysis Tool (KPAT). KPAT injects software probes into Linux kernel to track interested function calls and event occurrences in the kernel. Probe injection is done systematically and does not require recompiling the kernel. A module in KPAT finds the association between the tracked functions and protocol data captured by an independent sniffer. The result as an integrated log allows users to identify two-way relationship between protocol data and the execution sequence of network functions in the kernel. We successfully used KPAT to identity accurate latency of each handover phase in IEEE 802.11 wireless networks. Experimental results show that KPAT causes light overhead to the patched kernel.

This study is motivated by a plan to install a software-defined wireless local area network (SDWLAN) on campus, which possesses a desired property that both data flow and device behaviors can be software-definable. Because the installation involves hundreds of access points, we must conduct simulations beforehand to verify the design and scalability of the target system. However, existing SDN simulator like Mininet does not support modeling of wireless channel and mobility. On the other hand, common network simulator like ns-3 only has limited support for software-defined controllers and does not fully implement handover process. We thus develop OpenNet, which connects Mininet to ns3 to enjoy both Mininet's advantage of controller compatibility and ns-3's ability in the wireless/mobility modeling. OpenNet also complements ns-3 by adding probe mechanism, which is missing in the current ns-3 implementation. Our simulation result demonstrates the effectiveness of OpenNet.

Dominating set is a subset of nodes called dominators in a graph such that every non-dominator nodes (called dominatee) is adjacent to at least one dominator. This paper considers a more general multi-dominating problem where each node$i$, dominator or dominatee, is required to have at least$k_i$neighboring dominators, and different node can have different$k_i$value. We first propose a game design toward this problem. This game is self-stabilizing (i.e., it always ends up with a legitimate state regardless of its initial configuration). The obtained result is guaranteed minimal (i.e., it contains no proper subset that is also a multi-dominating set) and Pareto optimal (we cannot increase the payoff of some player without sacrificing the payoff of any other). We then point out challenges when turning the design into a distributed algorithm using guarded commands. We present an algorithm that is proved weakly stabilizing. Simulation results show that the proposed game and algorithm produce smaller dominating sets,$k$-dominating sets, and multi-dominating sets in various network topologies when compared with prior approaches.

Crossover nodes have been utilized to achieve smooth handoffs for micro mobility management schemes. IEEE 802.11s supports proxy-based frame delivery services for mobile hosts roaming within a wireless mesh network (WMN). Discovering crossover node for mobile hosts in 802.11s WMNs, however, faces several challenges due to intrinsic properties of mesh networks. This paper identifies these challenges and proposes a scheme suitable for crossover node discovery under 802.11s WMNs. This scheme is characterized by source-oriented, MAP-centric, and per-source. It can be performed off-line with the derived results cached for on-line retrievals during handoffs. Simulation results show that the proposed scheme can reduce packet losses due to handoffs, and confirm the necessity of the source-oriented principle.

Churn refers to a large number of arriving and departing participants within a short time in peer-to-peer (P2P) networks. This paper studies the impact of churn on real-world unstructured P2P networks. To this end, we need collecting snapshots of P2P network topology and associated performance metrics. Because P2P topology changes dynamically, the time to take a snapshot must be sufficiently short for the snapshots to be accurate. We propose Third-party-to-servent Crawling with Servent-to-servent Sampling (TCSS) system. TCSS uses a third-party crawling technique to collect network topology information without disturbing the original P2P network under investigation. Furthermore, TCSS adopts distributed and parallel techniques to speed up the crawling process. TCSS also employs a servent-to-servent sampling technique to gather the corresponding performance metrics of the P2P network simultaneously. Empirical results show that TCSS takes around 7 minutes to take a topology snapshot of the P2P network. Besides, we found that churn is indeed a combined effect of peer arrivals/departures and neighbor replacements. As the number of peers increases, the number of very long-lived peers remains nearly constant and the P2P network possesses a small-world property. Moreover, as churn aggravates, the average booting time of peers increases and the variation is proportional to the degree of churn. The response time of the top-rank keyword searches is not affected by the degree of churn.

Mobile devices equipped with two or more interfaces can roam across heterogeneous networks. This feature enables the devices to maintain service continuity by performing vertical handovers and to increase transmission capability by exploiting multiple physical transmission paths. However, this type of service continuity is not yet commonly provided. Previous approaches toward the provision of service continuity are limited either in their functionalities or in the scope of cross-layer information. In this paper, we analyze how cross-layer architecture provides service continuity from a broader perspective and propose Cooperative Layered Architecture (CoLA) implemented on Android system for service continuity during handover process. CoLA helps comprehensive information gathering from all layers for handover decisions, provides simple interfaces to make applications mobility-sensitive, and achieves efficient multipath transmissions across heterogeneous wireless links. Experimental results show that CoLA achieves its design goal in providing service continuity.

Radio interfaces and channels are two sorts of resources in a multi-channel, multi-radio wireless mesh network. An efficient allocation of radio resources to mesh devices should reduce co-channel interference for higher throughput while maintaining network connectivity. Unlike much research effort on such optimization dealing with link- or higher-level interference, this study is concerned with physical-layer interference. We propose a two-stage radio allocation scheme. The first stage assigns channels to radios using a game-theoretic approach while the second stage assigns the resulting radio-channel pairs to links using a greedy method. In the proposed game, wireless interfaces are modeled as players participating in a radio resource game with a utility function defined to minimize co-channel interference from other players. We prove that the game eventually reaches a pure-strategy Nash equilibrium regardless of the game's initial configuration. Simulation results indicate that the proposed scheme leads to more operative links than previous methods.

Wireless sensor nodes are usually densely deployed to completely cover (monitor) a set of targets. Consequently, redundant sensor nodes that are not currently needed in the covering task can be powered off to conserve energy. These sensors can take over the covering task later to prolong network lifetime. The coverage problem, concerns picking up a set of working sensors that collectively meet the coverage requirements. The problem is complicated by the possibility that targets may have different coverage requirements while sensor nodes may have different amounts of energy. This article proposes a game-theoretic approach to the coverage problem, where each sensor autonomously decides its state with a simple rule based on local information. We give rigorous proofs to show stability, correctness, and efficiency of the proposed game. Implementation variants of the game consider specific issues, such as game convergence time and different amounts of sensor energy. Simulation results show significant improvement in network lifetime by the proposed approach when compared with representative alternatives.

In a wireless mesh network, an efficient utilization of multiple radios with multiple channels involves the assignment of channels to radios/links. This becomes an optimization problem for which various objectives can be defined with various conflicting constraints and requirements. We present a novel channel assignment strategy based on predicted upper-bound and lower-bound of interference associated with particular assignments. An additional design is also proposed to prevent the possibility that two ends of any designated link are not assigned a common channel. Simulation results indicate that the proposed algorithm outperforms existing approaches in the number of operative links when only few channels or sufficiently many radios are provided.

In a tree-based ZigBee network, ZigBee routers (ZRs) must schedule their beacon transmission time to avoid beacon collisions. The beacon schedule determines packet delivery latency from the end devices to the ZigBee coordinator at the root of the tree. Traditionally, beacon schedules are chosen such that a ZR does not reuse the beacon slots already claimed by its neighbors, or the neighbors of its neighbors. We observe, however, that beacon slots can be reused judiciously, especially when the risk of beacon collision caused by such reuse is low. The advantage of such reuse is that packet delivery latency can be reduced. We formalize our observation by proposing a node-pair classification scheme. Based on this scheme, we can easily assess the risk of slot reuse by a node pair. If the risk is high, slot reuse is disallowed; otherwise, slot reuse is allowed. This forms the essence of our ZigBee-compatible, distributed, risk-aware, probabilistic beacon scheduling algorithm. Simulation results show that on average the proposed algorithm produces a latency only 24 percent of that with conventional method, at the cost of 12 percent reduction in the fraction of associated nodes.

This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call for data generated by all source nodes. The goal is to determine an optimal set of storage nodes that minimizes overall communication cost. Prior work toward this problem assumed homogeneous channel cost, which may not be the case in many network environments. We generalize the optimal storage problem for a tree-structured network by considering heterogeneous channel costs. The necessary and sufficient conditions for the optimal solution are identified, and an algorithm that incurs a linear time cost is proposed. We have also conducted extensive simulations to validate the algorithm and to evaluate its performance.

A number of IEEE 802.15.4 devices can be connected by a tree topology as proposed by ZigBee specification. Address configuration in tree-based ZigBee networks needs to assign every device a network address that uniquely identifies it from others, and such addressing should also assist routing. The addressing method recommended by the specification forces a static assignment that is coupled with node's location in the tree, resulting in an inflexibility in allocating addresses. This property may significantly decrease the ratio of addressable devices and cause routing detour. To alleviate the problem, this paper considers three alternatives that manage address space with flexibility but require additional storage in ZigBee routers. Performance evaluations indicate that proposed approaches provide different levels of tradeoff between the ratio of addressable devices and storage costs in ZigBee routers.

A k-connected wireless sensor network (WSN) allows messages to be routed via one (or more) of at least k node-disjoint paths, so that even if some nodes along one of the paths fail, or are compromised, the other paths can still be used. This is a much desired feature in fault tolerance and security, k-connectivity in this context is largely a well-studied subject. When we apply the random key pre-distribution scheme to secure a WSN however, and only consider the paths consisting entirely of secure (encrypted and/or authenticated) links, we are concerned with the secure k-connectivity of the WSN. This notion of secure k-connectivity is relatively new and no results are yet available. The random key pre-distribution scheme has two important parameters: the key ring size and the key pool size. While it has been determined before the relation between these parameters and 1-connectivity, our work in k-connectivity is new. Using a recently introduced random graph model called kryptograph, we derive mathematical formulae to estimate the asymptotic probability of a WSN being securely k-connected, and the expected secure k-connectivity, as a function of the key ring size and the key pool size. Finally, our theoretical findings are supported by simulation results.

In an IEEE 802.11 network, the traffic load collectively given by wireless stations (WSs) is usually not fairly shared by all available access points (APs), as WSs independently select APs to camp on. Prior approaches toward this problem either need to modify AP's behavior or require bandwidth negotiation and agreement enforcement between APs and WSs. These approaches are not practical due to their inability to apply to APs already in use. This paper proposes an application-layer approach, where a dedicated server is deployed to collects load-related information from APs utilizing SNMP (Simple Network Management Protocol). Our approach applies to off-the-shelf APs and has been proven very effective through thoughtful experiments.

This paper is concerned with caching support of access points (APs) for fast handoff within IEEE 802.11 networks. A common flavor of current schemes is to let a mobile station preauthenticate or distribute the security context of the station proactively to neighboring APs. Each target AP caches the received context beforehand and can save itself backend-network authentication if the station reassociates. We present an approach to ameliorating cache effectiveness under the least recently used (LRU) replacement policy, additionally allowing for distinct cache miss penalty indicative of authentication delay. We leverage the widely used LRU caching techniques to effect a new model where high-penalty cache entries are prevented from being prematurely evicted under the conven-tional replacement policy so as to save frequent, expensive authentications with remote sites. This is accomplished by introducing software-generated reference requests that trigger cache hardware machinery in APs to refresh certain entries in an automated manner. Performance evaluations are conducted using simulation and analytical modeling. Performance results show that our approach, when compared with the base LRU scheme, reduces authentication delay by more than 51 percent and cache miss ratio by over 28 percent on average. Quantitative and qualitative discussions indicate that our approach is applicable in pragmatic settings.

Bluetooth is a short-range TDD (time division duplex) wireless network that supports both circuit- and packet-oriented applications. A piconet is composed of a device configured as master and at most seven other devices acting as slaves. At medium access control (MAC) layer, the master can select a slave to send a data packet and until then, the slave is not allowed to transmit. Round-robin (RR) and exhaustive round-robin (ERR) are two elementary MAC scheduling schemes that are both simple and efficient. This paper proposes RR-FCFS, a simple MAC scheduling scheme that has the same advantages as RR and ERR. RR-FCFS acts as RR if the master's queue is empty and starts transmitting packets in first-come-first-serve order otherwise. The simulation results show that RR-FCFS's performance in terms of packet delay and queue length is comparable with those of RR and ERR.

This paper has analyzed link probability, expected node degree, expected number of links, and expected area collectively covered by a finite number of nodes in wireless ad hoc networks. Apart from the formulation of exact mathematical expressions for these properties, we have disclosed two fundamental results: (1) Every possible link has an equal probability of occurrence. (2) It is the border effects that makes two links probabilistically dependent. Simulation results show that our analysis predicts related measure with accuracy.

Orthogonal variable spreading factor (OVSF) codes have been proposed as the channelization codes used in the wideband CDMA access technology of IMT-2000. OVSF codes have the advantage of supporting variable bit rate services, which is important to emerging multimedia applications. The objective of OVSF code assignment algorithm is to minimize the probability of code request denial due to inappropriate resource allocation. In this paper, we propose an efficient OVSF code assignment scheme that utilizes multiple Rake combiners in user equipments. Our approach finds in constant time all feasible codewords for any particular request, trying to minimize both rate wastage and code fragments. The simulation result shows that our scheme outperforms previous work in the probability of request denial. The code management overhead is also minimal in our scheme.

Detecting global predicates is an important task in testing and debugging distributed programs. In this paper, we propose an approach that effectively precludes useless events for global predicate detection, facilitating the process of an independent online checking routine. To identify more useless events than a simple causality-check method can do, our method tracks and maintains the precedence information of event intervals as a graph. To reduce the potentially expensive space and time costs as the graph expands, we propose an effective scheme to prune the graph. The performance of our method is analyzed and evaluated by simulations. The result shows that our approach outperforms conventional approaches in terms of the number of useless events found.