A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks

Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to users’ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability

Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig