Hybrid Training With Binary Search Protocol for Wireless Sensor Networks

Locationing problem in Wireless Sensor Networks(WSN) can be viewed as a general distributed sensor problem. It is with sensors that can discover other nodes or estimate ranges between nodes. that serve as position references. In this paper. we show that sensors acquire coarse-grain location awareness by the training protocol. The training protocol which hybrids the synchronization and training procedure. In this protocol, synchronization and training are combined into one scheme. The sink node sends two beacons in each slot instead of one. In the training, sensor searching for its location using a binary search scheme. Our simulation results shown less number of cycles needed for training

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

Towards the fast and robust optimal design of Wireless Body Area Networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.Comment: Authors' manuscript version of the paper that was published in Applied Soft Computin

Energy-Efficient Self-Organization Protocols for Sensor Networks

A Wireless Sensor Network (WSN, for short) consists of a large number of very small sensor devices deployed in an area of interest for gathering and delivery information. The fundamental goal of a WSN is to produce, over an extended period of time, global information from local data obtained by individual sensors. The WSN technology will have a significant impact on a wide array of applications on the efficiency of many civilian and military applications including combat field surveillance, intrusion detection, disaster management among many others. The basic management problem in the WSN is to balance the utility of the activity in the network against the cost incurred by the network resources to perform this activity. Since the sensors are battery powered and it is impossible to change or recharge batteries after the sensors are deployed, promoting system longevity becomes one of the most important design goals instead of QoS provisioning and bandwidth efficiency. On the other hand the self-organization ability is essential for the WSN due to the fact that the sensors are randomly deployed and they work unattended. We developed a self-organization protocol, which creates a multi-hop communication infrastructure capable of utilizing the limited resources of sensors in an adaptive and efficient way. The resulting general-purpose infrastructure is robust, easy to maintain and adapts well to various application needs. Important by-products of our infrastructure include: (1) Energy efficiency: in order to save energy and to extend the longevity of the WSN sensors, which are in sleep mode most of the time. (2) Adaptivity: the infrastructure is adaptive to network size, network topology, network density and application requirement. (3) Robustness: the degree to which the infrastructure is robust and resilient. Analytical results and simulation confirmed that our self-organization protocol has a number of desirable properties and compared favorably with the leading protocols in the literature

Machine learning approach for detection of nonTor traffic

Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset