Fast growing age demographics, sedentary lifestyle, increasing chronic diseases, shortage of medical facilities and huge costs for healthcare treatment have arisen the need for ubiquitous as well as inexpensive healthcare services. A huge part of the cost in providing healthcare is utilized in frequent patient’s visits and continual monitoring of the chronically ill patients. Wireless Body Area Network (WBAN) has offered promising outcomes for Quality of Service (QoS) in the healthcare system. Nevertheless, it is constrained with restricted battery power of sensors, security and privacy issues for its complete adoption. Henceforth, security, privacy and energy efficiency issues need to be addressed well with optimal solutions. The current work proposes an Optimized Energy Efficient Secure Routing Protocol (OEESR) that minimizes network congestion, provides secure data transmission and selects the most optimal route in the network. The performance of OEESR protocol is contrasted with other state-of-art protocols. The results show that OEESR is highly secure and achieves 90% throughput with 8.8% of overall energy consumption. It is having only 13% of packet dropping rate with respect to transmission power.

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.

The paper presents a new security aspect for a Mobile Ad-Hoc Network (MANET)-based IoT model using the concept of artificial intelligence. The Black Hole Attack (BHA) is considered one of the most affecting threats in the MANET in which the attacker node drops the entire data traffic and hence degrades the network performance. Therefore, it necessitates the designing of an algorithm that can protect the network from the BHA node. This article introduces Ad-hoc On-Demand Distance Vector (AODV), a new updated routing protocol that combines the advantages of the Artificial Bee Colony (ABC), Artificial Neural Network (ANN), and Support Vector Machine (SVM) techniques. The combination of the SVM with ANN is the novelty of the proposed model that helps to identify the attackers within the discovered route using the AODV routing mechanism. Here, the model is trained using ANN but the selection of training data is performed using the ABC fitness function followed by SVM. The role of ABC is to provide a better route for data transmission between the source and the destination node. The optimized route, suggested by ABC, is then passed to the SVM model along with the node’s properties. Based on those properties ANN decides whether the node is a normal or an attacker node. The simulation analysis performed in MATLAB shows that the proposed work exhibits an improvement in terms of Packet Delivery Ratio (PDR), throughput, and delay. To validate the system efficiency, a comparative analysis is performed against the existing approaches such as Decision Tree and Random Forest that indicate that the utilization of the SVM with ANN is a beneficial step regarding the detection of BHA attackers in the MANET-based IoT networks.

With the technological advancements in the field of Wireless Sensor Networks (WSNs) in the last decade, a number of remote monitoring and health care applications have made their presence felt through implanted and wearable biosensors. These biosensors can be used for real time patient monitoring creating a Wireless Body Area Sensor Network (WBASN). These biosensors are equipped with limited battery creating a requirement for energy-efficient schemes for WBASN protocols. The biosensor nodes are heterogeneous in terms of buffer size, traffic type, inter-arrival time and payload size. The data generated by these nodes may have different requirements based on their quality parameters. A biosensor node generating electroencephalography (EEG) reading may require reliable transmission of data and same way some other biosensor node may generate delay sensitive data. Differing requirements of these biosensor nodes lead to the requirement of QoS aware schemes for WBASN. In this paper, an energy-efficient Quality of Service (QoS) aware time slot allocation scheme is proposed for WBASN Media Access Control (MAC) layer. This scheme takes heterogeneity of biosensor nodes under consideration. Slot allocation is based on QoS aware priority classes. Unlike most of the existing schemes, time slot duration is traffic adaptive in this sch eme and variable length slots are allocated based on different payload sizes of the sensor nodes. This scheme improves the energy efficiency by reducing the chances of packet drops and energy wastage due to idle listening. Sleep time for sensor nodes is increased leading to lower energy consumption. The proposed time slot allocation scheme is compared with slot allocation scheme of eMC-MAC protocol. Simulation result shows a significant improvement in energy efficiency over eMC-MAC slot allocation scheme.