Establishing Self-Healing and Seamless Connectivity among IoT Networks Using Kalman Filter

The Internet of Things (IoT) is the extension of Internet connectivity into physical devices and to everyday objects. Efficient mobility support in IoT provides seamless connectivity to mobile nodes having restrained resources in terms of energy, memory and link capacity. Existing routing algorithms have less reactivity to mobility. So, in this work, a new proactive mobility support algorithm based on the Kalman Filter has been proposed. Mobile nodes are provided with a seamless connectivity by minimizing the switching numbers between point of attachment which helps in reducing signaling overhead and power consumption. The handoff trigger scheme which makes use of mobility information in order to predict handoff event occurrence is used.  Mobile nodes new attachment points and its trajectory is predicted using the Kalman-Filter. Kalman-Filter is a predictor-estimator method used for movement prediction is used in this approach. Kalman Filtering is carried out in two steps: i) Predicting and ii) Updating. Each step is investigated and coded as a function with matrix input and output. Self-healing characteristics is being considered in the proposed algorithm to prevent the network from failing and to help in efficient routing of data. Proposed approach achieves high efficiency in terms of movement prediction, energy efficiency, handoff delay and fault tolerance when compared to existing approach

Novel evaluation framework for sensing spread spectrum in cognitive radio

The cognitive radio network is designed to cater to the optimization demands of restricted spectrum availability. A review of existing literature on spectrum sensing shows that there is still a broader scope for its improvement. Therefore, this paper introduces an efficient computational framework capable of evaluating the effectiveness of the spread spectrum concept in the context of cognitive radio network in a more scalable and granular way. The proposed method introduces a dual hypothesis using a different set of dependable parameters to emphasize the detection of optimal energy for a low signal quality state over the noise. The proposed evaluation framework is benchmarked using a statistical analysis method not present in any existing approaches toward spread spectrum sensing. The simulated outcome of the study exhibits that the proposed system offers a significantly better probability of detection than the current system using a simplified evaluation scheme with multiple test parameters

IoT applications and services in space information networks

The rate of traffic generated by MTC is growing at a very fast pace, putting strong pressure on existing network infrastructures. One of the most challenging tasks today is being able to support M2M/IoT data exchanges by providing connectivity between any pair of M2M devices all over the world. For this reason, we analyze the role of SINs in supporting MTC in this work. Horizontal solutions are analyzed herein in order to allow interworking by acting as relay entities among different protocol stacks and services, vertically implemented over different network segments. We analyze the still pending challenges hampering interworking, and propose a possible protocol stack for M2M/IoT communications based on the oneM2M standard. Eventually, this article compares the performance achievable by using two of the most diffused application protocols, CoAP and MQTT, shedding light on their efficiency and differences