Federated Learning is a promising option for data privacy and security in ITS, because it allows edge devices, Road Side Units (RSUs), and Central Server (CS) to jointly train the machine learning model. Since RSU collects data from the vehicles passing through its range, the local data of each RSU will have a non-IID distribution, which adversely affects the convergence speed and accuracy of FL training. Generating synthetic data locally at individual nodes, followed by data shuffling among the nodes, is a promising approach to address the Non-IID data problem. In this work, we propose pliable index coding (PIC) solutions for efficient data shuffling among the nodes in an FL system. In PIC($S$) problems, a client is satisfied if it can retrieve any $S$ new messages not originally present in its side-information. We particularly consider decentralized pliable index coding problems (DPIC) where the clients communicate among themselves without a central server to model the data shuffling in FL. A class of DPIC, known as Consecutive Decentralized Pliable Index Coding (CDPIC($S$,$K$)), where each client has $K$ consecutive messages as side-information, is considered. For CDPIC($S$,$K$) problems, pliable index code designs are provided for any value of $K$ and $S$, and optimality proofs for some of the cases are established. Further, these CDPIC solutions are applied for data shuffling in FL, to transform the local data distribution towards IID progressively with each transmission, thereby enhancing the performance of FL. The improvement in the accuracy and convergence of the most popular FL technique, FedAvg, and a promising federated submodel technique, CELL (Communication Efficient Lottery Learning), are analysed by providing different degrees of data shuffling using the proposed CDPIC schemes.

The privacy of medical data and resource restrictions in the Internet of Things (IoT) nodes prohibit medical users from utilizing disease detection (DD) services offered by the health cloud in the mobile healthcare network (MHN). Also, health clouds may need the DD procedures to be private. Therefore, the essential requirements for MHN DD services are (i) performing accurate and fast DD without jeopardizing the privacy of health clouds and medical users and (ii) reducing the computational and transmission overhead (energy-consumption) of the green IoT devices while performing privacy-preserving DD. The outsourced privacy-preserving DD is available in the literature based on popular tree-based machine learning schemes such as a random forest. However, these schemes utilize energy-hungry public-key encryption schemes in IoT nodes at medical users for privacy preservation. This work proposes an energy-efficient, fully homomorphic modified Rivest scheme (FHMRS) for the proposed privacy-preserving random forest classification (PRFC). A secure integer comparison protocol is also developed for reducing processing time and energy consumption for users while performing outsourced PRFC. The implementation results and security analysis show that the proposed schemes guarantee better energy efficiency for MHN green IoT devices without compromising privacy than the existing tree-based schemes.

Multi-carrier Non Orthogonal Multiple Access system is considered a promising technique for future wireless communication systems. MC-NOMA system divides transmission bandwidth into sub-bands and multiple users in each sub-band are served based on power-domain NOMA. This work attempts to find the best clustering scheme that can provide the maximum sum rate for NOMA with unequal bandwidth allocation. We try to improve the system performance by tuning the bandwidth allocated to different clusters to find the best clustering scheme. The analytical results compared and validated using the golden search method reveal that pairing up the best channel gain user with the next best channel gain user with optimal bandwidth allocation gives a better sum rate compared to competitive schemes available in the literature.

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

The demand for multimedia services is growing day by day in vehicular ad-hoc networks (VANETs), resulting in high spectral usage and network congestion. Non-orthogonal multiple access (NOMA) is a promising wireless communication technique to solve the problems related to spectral efficiency effectively. The index coding (IC) is a powerful method to improve spectral utilization, where a sender aims to satisfy the needs of multiple receivers with a minimum number of transmissions. By combining these two approaches, in this work, we propose a novel technique called index coded NOMA (IC-NOMA), where we apply NOMA techniques on index coded data to reduce the number of transmissions further. This work shows that the IC-NOMA system demands a specific design for index codes to reap the advantages of NOMA. We have done the feasibility analysis of the proposed method in a general scenario and proposed an index code design to integrate IC over NOMA for the best efficiency. The performance gains of proposed system compared to conventional IC system is ilustrated in terms of power efficiency and spectral efficiency.

We consider a particular category of the index coding problems in the network to vehicle (N2V) communication scenario where each vehicle possesses some messages as side information and demands all messages which it does not possess. Under this setup, a lower bound on the number of transmissions is determined. This work proposes the design of an optimal index code for a particular case of N2V communication where the side information is consecutive and present a practical VANET scenario, where the proposed index code (IC) could be applied. The proposed IC solution is independent of the field size and can operate in any field. We have applied index-coded modulation (ICM) over the proposed IC for noisy broadcasting on the AWGN broadcast channel for improved bandwidth efficiency. An algorithm is proposed to map the index coded vectors to constellation points in such a way that the benefit in the error performance can be achieved by a maximum number of vehicles. The simulation results show that ICM offers improved error performance compared to Gray coded modulation for many vehicles while not degrading the performance for others.

Vehicular ad hoc network (VANET), is a developing platform with massive data demands for infotainment services in recent years. Index Coded NOMA (IC-NOMA) is a spectral efficient transmission method that can be used in VANETs. IC-NOMA applies the concepts of non-orthogonal multiple access (NOMA) over the index coded data to increase spectrum and power efficiency. In NOMA, far user does not get access to the near user data, while near user can successfully decode far user data. Therefore, the IC-NOMA demands a novel design of index code for improved bandwidth efficiency. This work considers the design of index code for NOMA when the user demands in VANET follows the data distribution of one-sided symmetric neighboring consecutive side information single unicast index coding problem (SNC-SUICP). For this setup, we develop an optimal closed form index coding (IC) solution which can bring in additional bandwidth savings through NOMA. The improved performance of the proposed IC-NOMA transmission scheme when compared with one-sided SNC-SUICP in terms of bandwidth efficiency is demonstrated.

Vehicle to vehicle (V2V) communication has gained its importance in recent years. In this work we consider the index coding problem (ICP) over noisy channel in V2V communication phase of message dissemination. The ICP in V2V communication is considered as device to device ICP with transmitting nodes as receiving nodes also. The index code is used in noisy scenario over AWGN channel and the broadcast vectors are mapped to suitable M-PSK signal constellations to save bandwidth, thus increasing the bandwidth efficiency. An algorithm is proposed to derive suitable mapping of the broadcast vectors to M-PSK signal constellation for improving the error performance. Performance improvement in terms of PSK index coding gain is discussed. While one vehicle transmits, the proposed algorithm provides performance improvement for at least one of the remaining vehicles, in most of the scenarios, while not penalizing the other vehicles.

This study proposes a secure and privacy‐preserving protocol for outsourcing health data processing operations during the emergency in the mobile healthcare network. The proposed protocol provides a practical solution to utilise smartphone resources at both remote and nearby for processing the overwhelming personal health information (PHI) of a user in healthcare emergency opportunistically and securely. The patients with symptoms matching with those of the user in an emergency are considered as opportunities to minimise the privacy disclosure of the user. Opportunities at both remote and nearby are exploited with the help of a base station in the 4G network. Moreover, novel and efficient outsourced privacy access control schemes are developed to minimise the power drain of the user in an emergency without compromising his privacy. The outsourced privacy access control is facilitated through the design of innovative schemes for outsourced attribute‐based access mechanism and an outsourced privacy‐preserving scalar product computation. Detailed performance evaluations through implementations on Raspberry Pi 3B + board and simulations using NS3 network simulator and Scyther tool confirm the efficiency of the proposed protocol in providing highly reliable PHI processing and transmissions with reasonably low delay and energy consumption while maintaining user privacy.

This paper presents a new framework for wireless video streaming services using the resource-constrained embedded camera sensors based on block compressive sensing. We propose adaptive encoding scheme that exploits high redundancy between successive video frames to reduce transmission cost while effectively handling occlusion effects and, at the same time, maintains the simple and energy conserving encoder design. The proposed methodology adapts the compression ratio for different blocks of the non-key frame depending on temporal correlation. We also propose compressed domain motion and measurements estimation techniques to exploit the high correlation between successive frames at the decoder. The proposed motion estimation technique makes use of restricted isometry property of the sensing matrix to seek the best matching measurement vector for motion estimation as opposed to block matching in conventional video coding. In the proposed measurement estimation technique, efficient utilization of bandwidth is achieved by skipping some measurements at the transmitter side. The skipped measurements are estimated at the receiver by exploiting the correlation between CS measurements of the non-key frame and corresponding motion predicted frame using multiple regression model. Extensive simulation results on a set of diverse video sequences are presented to demonstrate the effectiveness of the proposed video coding technique.

Abstract This paper presents a key distribution scheme based on elliptic curve Diffie–Hellman (ECDH) key exchange for static wireless sensor networks with node addition. The proposed scheme relies on the transitory master key approach for secret key establishment and addresses the man‐in‐the‐middle attack in ECDH key exchange. The major advantage of the proposed method compared with the state‐of‐the‐art schemes is the high resilience offered by this scheme even if nodes are compromised in the initialisation phase. The communication overhead, computational cost and storage requirement of the proposed method is very less compared with the elliptic curve‐based approaches available in the literature. The proposed scheme can also support large networks without affecting the security and efficiency. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract The major requirements of a secure high‐speed communication system are low delay, good error performance, low redundancy, and good security. A simple nonlinear filter generator (NLFG)‐based cryptosystem is proposed in this paper to provide low delay with high security. Enhanced security is attained by embedding security in the channel encoder. An efficient concatenation of Reed–Solomon code and a secure quasi‐cyclic low density parity check code is used as channel encoder in the proposed cryptosystem to provide good error performance with low redundancy and to enhance security of NLFG with low structural complexity. The proposed efficient design of key‐based dense scrambling matrix and permutation matrix greatly simplifies the hardware structure. The novel method for generation of intentional error vector with modification of NLFG helps to reduce both the delay and power consumption. The integrated system is designed in such a way that both NLFG‐based stream cipher and secure channel coder complement each other to overcome the cryptographic weakness with a low hardware cost. The analytical and simulation results show that the proposed system attains significant security advantage against typical attacks and offers very good error performance. Copyright © 2015 John Wiley & Sons, Ltd.