Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee’s health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.

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6LoWPAN is for IPv6 packets to be sent to and received from over IEEE 802.15.4 based networks for applications which require wireless internet connectivity at lower data rates for devices. However it is well known that the management of addresses for devices that communicate across the two dissimilar domains of IPv6 and IEEE802.15.4 is complicated. Routing itself is another problem especially between the IPv6 domain and the IEEE802.15.4 domain. IEEE802.15.4 standard packet size is 127 bytes, among which IEEE 64 bit extended addresses may be used. After an association, 16 bits are used as an unique ID in a PAN L2, Still only 102 bytes are available for payload at MAC layer. Now considering the devices need to communicate with other nodes via IPv6 domain, 256 bits of the source and destination addresses seem to be cumbersome in a limited MAC payload fields. Here we propose an IP address translation mechanism. When a device in IEEE802.15.4 domain needs to communicate with other nodes in IPv6 domain, it acquires its source ID and destination ID for source IP address and destination IP address, respectively from an IP translationcapable gateway. The device will send the packet using those ID's to the gateway, and then the gateway will translate it to normal IPv6 packet. This paper will show the detailed procedures and its performance.

As the wireless mobile devices are coming to wide use, MANETs are getting more attention. Each device has to not only maintain some information relating to forwarding the traffic including the traffic unrelated to its own use, but also establish the path using the broadcast packets. The more traffic, the more channel contentions, redundant retransmissions and collisions, which will cause to increase the battery consumption, which is one of critical factors of each small device. Furthermore there is another factor that energy consumption should be evenly distributed among small devices, since some power-used-up nodes will cause traffic delivery delay or block. Here we propose a FES algorithm in MANET-like environment, showing power consumption can be saved and evenly distributed to nodes in MANET. The simulations were performed using the Qualnet 5.1 discrete event network simulator.

Fourier modal method based quantitative analysis method of optical power flow and energy loss in general multi-block photonic structures with an internal dipole emitter is described. The analytic expressions of modal power flow and loss are derived for accurate and efficient quantitative analysis. It is revealed that a few dominating excited photonic modes substantially govern the internal energy flow and energy loss. The optical characteristics of the dominant modes are investigated.