The dynamics of Mobile Ad-hoc NETworks (MANETs), as a consequence of mobility of mobile hosts, pose a problem in finding stable multi-hop routes for communication between a source and a destination. Traditional ad-hoc routing protocols are proposed to find multi-hop routes based on shortest path routing algorithms, which cannot effectively adapt to time-varying radio links and network topologies of MANETs. In this paper, a novel stability-based ad hoc routing protocol, which is named as Ad-hoc On-demand Stability Vector (AOSV) routing protocol, is proposed to properly and effectively discover stable routes with high data throughput and long lifetime by considering the radio propagation effect on signal strength. Here, a stochastic mobile-to-mobile radio propagation model is proposed to predict path loss as well as received signal strength between adjacent nodes, and the estimation of link/route stability is derived from the prediction of signal strength. With awareness of link and route stabilities, a path finding algorithm is designed to explore the stable route with largest route stability for a source and destination pair. The performance of AOSV protocol is compared with the well-known Ad-hoc On-demand Distance Vector (AODV) routing protocol and other related works. Simulation results indicate that the AOSV routing protocol leads to significant throughput increases up to 70% improvement comparing to AODV, and provides better performance than related works.

We study the interaction of TCP and the proportional fair scheduling algorithm in wireless networks. We show that the additive increase and multiplicative decrease algorithm of TCP can favor bad channel users, which results in inefficient use of radio resources. To remedy the problem, a proportional queue management scheme is proposed. The effectiveness of the algorithm is shown by simulations.

As the number of electronic control units (ECUs) or sensors connected to a controller area network (CAN) bus increases, so does the bus load. When a CAN bus is overloaded by a large number of ECUs, both the waiting time and the error probability of the data transmission are increased. Because the duration of the data transmission is proportional to the frame length, it is desirable to reduce the CAN frame length. In this paper, we present an improved CAN data-reduction (DR) algorithm to reduce the amount of data to be transferred in the CAN frame length. We also implement the data reduction algorithm using the CANoe software, and measure the CAN bus load using a CANcaseXL device. Experimental results with a Kia Sorento vehicle indicate that we can obtain additional average compression ratio of 11.15% with the proposed method compared with the ECANDC algorithm. By using the CANoe software, we show that the average message delay is within 0.10ms and the bus load can be reduced by 23.45% with 20 ECUs using the proposed method compared with the uncompressed message.

In this paper, the artificial noise (AN)-aided multiple-input single-output (MISO) cognitive radio network with simultaneous wireless information and power transfer (SWIPT) is considered, in which the cognitive user adopts the power-splitting (PS) receiver architecture to simultaneously decode information and harvest energy. To support secure communication and facilitate energy harvesting, AN is transmitted with information signal at cognitive base station (CBS). The secrecy energy efficiency (SEE) maximization problem is formulated with the constraints of secrecy rate and harvested energy requirements as well as primary user's interference requirements. However, this challenging problem is non-convex due to the fractional objective function and the coupling between the optimization variables. For tackling the challenging problem, a double-layer iterative optimization algorithm is developed. Specifically, the outer layer invokes a one-dimension search algorithm for the newly introduced tight relaxation variable, while the inner one leverages the Dinkelbach method to make the fractional optimization problem more tractable. Furthermore, closed-form expressions for the power of information signal and AN are obtained. Numerical simulations are conducted to demonstrate the efficiency of our proposed algorithm and the advantages of AN in enhancing the SEE performance.

Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.