DATA MINING FOR INTERFERENCE AVOIDANCE IN SMART CITIES IOT NETWORKS

A rapid growth of the wireless communications and heavily occupied spectrum lead to an inevitable interference between the heterogenous systems operating in the same frequency band. Having in mind the development of the Internet of Things (IoT) services and networks and widely present WiFi networks on the one hand, and the fact that these two systems occupy the same 2.4 GHz frequency band on the other hand, it is clear that the control of the interference and the spectrum coordination are of the highest importance. The first step in the interference control is to acquire its properties. Since the simulation of a large IoT network is not entirely possible, due to the numerous factors not known in advance, the interference assessment is performed on the SmartSantander, an IoT testbed, located in Santander, Spain. This paper presents a statistical analysis of the sensor data and describes the interference properties and its influence. These results may be used for the spectrum coordination, together with the neural networks and semantic technologies

Energy efficient in cluster head and relay node selection for wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way to sense and control the surrounding environment. However, nodes contain limited energy which is the key limiting factor of the sensor network operation. In WSN architecture, the nodes are typically grouped into clusters where one node from each cluster is selected as the Cluster Head (CH) and relays utilisation to minimise energy consumption. Currently, the selection of CH based on a different combination of input variables. Example of these variables includes residual energy, communication cost, node density, mobility, cluster size and many others. Improper selection of sensor node (i.e. weak signal strength) as CH can cause an increase in energy consumption. Additionally, a direct transmission in dual-hop communication between sensor nodes (e.g. CH) with the base station (BS) uses high energy consumption. A proper selection of the relay node can assist in communication while minimising energy consumption. Therefore, the research aim is to prolong the network lifetime (i.e. reduce energy consumption) by improving the selection of CHs and relay nodes through a new combination of input variables and distance threshold approach. In CH selection, the Received Signal Strength Indicator (RSSI) scheme, residual energy, and centrality variable were proposed. Fuzzy logic was utilized in selecting the appropriate CHs based on these variables in the MATLAB. In relay node selection, the selection is based on the distance threshold according to the nearest distance with the BS. The selection of the optimal number of relay nodes is performed using K-Optimal and K-Means techniques. This ensures that all CHs are connected to at least one corresponding relay node (i.e. a 2-tier network) to execute the routing process and send the data to BS. To evaluate the proposal, the performance of Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) was compared based on 100, 200, and 800 nodes with 1 J and random energy. The simulation results showed that our proposed approach, refer to as Energy Efficient Cluster Heads and Relay Nodes (EECR) selection approach, extended the network lifetime of the wireless sensor network by 43% and 33% longer than SEP and MAP, respectively. This thesis concluded that with effective combinations of variables for CHs and relay nodes selection in static environment for data routing, EECR can effectively improve the energy efficiency of WSNs

An Adaptive WLAN Interference Mitigation Scheme for ZigBee Sensor Networks

We propose an adaptive interference avoidance scheme that enhances the performance of ZigBee networks by adapting ZigBees' transmissions to measured wireless local area network (WLAN) interference. Our proposed algorithm is based on a stochastic analysis of ZigBee operation that is interfered with by WLAN transmission, given ZigBee and WLAN channels are overlaid in the industrial, scientific, and medical (ISM) band. We assume that WLAN devices have higher transmission power than ZigBee devices. Then, the high transmission power of WLAN devices causes the capture effect when WLAN and ZigBee transmit simultaneously. On the other hand, ZigBee performs backoff during clear channel assessment (CCA) operation if the WLAN is transmitting its frames on the channel. We adopt a widely used WLAN queueing/transmission model that is based on a Markov chain concept. We model a ZigBee device's operation using the Markov chain that includes WLAN interference statistically derived from the WLAN queueing/transmission model. Our proposed algorithm is evaluated in a simulated ZigBee network in the presence of varying WLAN interference. Numerical results show that our WLAN interference mitigation scheme finds the ZigBee control parameters, among a candidate set, which enhances ZigBee network performance compared to the conventional ZigBee operation