This paper presents APT, a localization system for outdoor pedestrians with smartphones. APT performs better than the built-in GPS module of the smartphone in terms of accuracy. This is achieved by introducing a robust dead reckoning algorithm and an error-tolerant algorithm for map matching. When the user is walking with the smartphone, the dead reckoning algorithm monitors steps and walking direction in real time. It then reports new steps and turns to the map-matching algorithm. Based on updated information, this algorithm adjusts the user's location on a map in an error-tolerant manner. If location ambiguity among several routes occurs after adjustments, the GPS module is queried to help eliminate this ambiguity. Evaluations in practice show that the error of our system is less than 1/2 that of GPS.

In many applications of wireless sensor networks, a sensor node senses the environment to get data and delivers them to the sink via a single hop or multi-hop path. Many systems use a tree rooted at the sink as the underlying routing structure. Since the sensor node is energy constrained, how to construct a good tree to prolong the lifetime of the network is an important problem. We consider this problem under the scenario where nodes have different initial energy, and they can do in-network aggregation. In previous works, it has been proved that finding a maximum lifetime tree from all feasible spanning trees is NP-complete. Since delay is also an important element in time-critical applications, and shortest path trees intuitively have short delay, it is imperative to find a shortest path tree with long lifetime. This paper studies the problem of maximizing the lifetime of data aggregation trees, which are limited to shortest path trees. We find that when it is restricted to shortest path trees, the original problem is in P. We transform the problem into a general version of semi-matching problem, and show that the problem can be solved by min-cost max-flow approach in polynomial time. Also we design a distributed solution. Simulation results show that our approach greatly improves the lifetime of the network and is more competitive when it is applied in a dense network.

Area coverage is one of the most common and important tasks for Flying Ad Hoc Networks (FANETs). The increasingly large scale of FANETs brings challenges in communication and coverage. Clustering is an effective technique for networking and management for large-scale ad hoc networks. Meanwhile, some applications, i.e., face recognition, need to perform intensive computation after UAVs perform area coverage. Due to long response delay in transferring data to the cloud, it becomes a trend to use Mobile Edge Computing (MEC) for processing data in FANETs, which selects the node of rich computing resources, i.e., cluster head, as MEC server, thus the delay performance of the edge node to the server is particularly critical. However, there is a conflict between area coverage efficiency and delay performance. Area coverage expects UAVs to spread as widely as possible, which may lead to longer delay. In this paper, we consider maximizing coverage efficiency under delay constraints. We define the coverage efficiency and propose an iterative Coverage Efficient Clustering Algorithm (CECA) by applying penalty and block coordinate descent methods. Specifically, the cluster heads, positions and transmit powers are alternately optimized in each iteration. In addition, CECA can adjust delay constraints according to task requirements. Extensive simulation results show that our proposed approach is superior to other approaches in terms of coverage efficiency and delay.

In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.

Cognitive radio (CR) networks have an ample but dynamic amount of spectrum for communications. Communication rendezvous in CR networks is the process of establishing a control channel between radios before they can communicate. Designing a communication rendezvous protocol that can take advantage of all the available spectrum at the same time is of great importance, because it alleviates load on control channels, and thus further reduces probability of collisions. In this paper, we present ETCH, efficient channel-hopping-based MAC-layer protocols for communication rendezvous in CR networks. Compared to the existing solutions, ETCH fully exploits spectrum diversity in communication rendezvous by allowing all the rendezvous channels to be utilized at the same time. We propose two protocols, SYNC-ETCH, which is a synchronous protocol assuming CR nodes can synchronize their channel hopping processes, and ASYNC-ETCH, which is an asynchronous protocol not relying on global clock synchronization. Our theoretical analysis and ns-2-based evaluation show that ETCH achieves better performances of time-to-rendezvous and throughput than the existing work.

As the first step of the communication procedure in 802.11, an unwise selection of the access point (AP) hurts one client's throughput. This performance downgrade is usually hard to be offset by other methods, such as efficient rate adaptations. In this paper, we study this AP selection problem in a decentralized manner, with the objective of maximizing the minimum throughput among all clients. We reveal through theoretical analysis that the selfish strategy, which commonly applies in decentralized systems, cannot effectively achieve this objective. Accordingly, we propose an online AP association strategy that not only achieves a minimum throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with reasonable computation and transmission overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments and intensive simulations.