The persistent digital divide, characterized by un-equal access to affordable and reliable mobile connectivity, remains a critical challenge for inclusive global development. The flow of network resources remains a complex task due to multi-vendor policies in beyond 5G networks (B5G) and sparse user distributions in remote areas. Addressing this issue, this paper presents a cost-aware, game-theoretic framework that leverages software-defined network slicing (SDNS) to minimize deployment cost while maintaining service quality across distributed and sparsely connected user entities (UEs). The problem considers multiple mobile infrastructure providers (MIPs) offering network slices at different cost levels and mobile virtual network operators (MVNOs) serving end-users seeking optimal connectivity at reasonable prices. The slice allocation and pricing formulation is cast as a mixed-integer nonlinear optimization problem (MILP) that is NP-hard due to the combinatorial coupling of users, slices, and link capacities. We decompose the problem into tractable subgames using a non-cooperative game-theoretic approach that converges to a near-optimal Nash equilibrium. The equilibrium ensures fair profit distribution among MIPs while providing affordable access to users. Simulation results over sparse network topologies demonstrate improved throughput and SINR characteristics with cost savings up to 25% compared to random allocation. Profitability indices show balanced revenue across providers, validating the efficiency of the proposed distributed algorithm in bridging the digital divide.

The network slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g., 5G and 6G). Software-defined networking (SDN) and network functions virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-SDN (vSDN) environment, a chain of well-defined virtual network functions (VNFs) are installed on service function chains (SFCs) by multiple Internet service providers (ISPs) concurrently. Generation, allocation, reallocation, release, and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Toward solving this fundamental issue, in this work, we have proposed a multilayered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-embedding and allocation problem (VNF-EAP) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Privacy of critical locations (or events) is essential when monitored by wireless sensor networks. To mitigate such issues, in this article, a new privacy protection technique named position-independent and section-based source location privacy (PSSLP) is developed. A biased random walk and greedy walk using a three- or four-phase routing strategy is employed here, where the number of phases depends on the network segment in which the source is situated. The biased random walk is intended to send packets away from the source of information and make routing paths appear dynamic to the eavesdropper, whereas, the greedy routing ensures that the packets converge at the base station. The objective of the solution is to achieve a uniform amount of privacy irrespective of the position of the asset in the network without compromising the network lifetime. Performance evaluation is done using developed analytical models and simulation results reveal that PSSLP achieves 8247.06- and 33.0- folds improvement in terms safety period and network lifetime, respectively, compared to no SLP protection technique (i.e., shortest path routing technique).

The creation of non-invasive Neuro-headset technology has resulted in a variety of applications such as control, automation, and vehicular control. Unmanned Aerial Vehicles (UAVs), mostly used in aerial communication, is a growing field of study that may be applied to both defense and commercial applications. As the need for drone control grows, recent advancements in the BCI-based drone control system have been made. This paper proposes a unique prototype to achieve smooth and stable control of a UAV by the human brain based on a motor imagery signal for semi-autonomous navigation. In such cases the accuracy and signal detection and response time get the utmost priority to get a decent performance of the vehicle. Currently, it is very common to use CSP for the classification of motor imagery data. However, this algorithm needs improvement in the low quantity of training samples and noisy data. Because in that scenario the over-fitting may be a problem. To overcome those drawbacks this study proposed Stacked Ensemble Common Spectral Pattern (SECSP). Comparing this method with Common spatial pattern (CSP), Filter Bank CSP (FBCSP), and Common Spatio-Spectral Patterns (CSSP) it has been proved that our model outperforms those popular methods in terms of accuracy, sensitivity, robustness, and precision for motor imagery Electroencephalogram data. The publicly available data-set for motor imagery BCI Competition IV, Data-set 1, BCI Competition III, Data-set IVA have all been tested with Stacked Ensemble CSP (SECSP). Performance is enhanced when compared to previous approaches, with an average accuracy of 87.74 percent for all subjects and 95.64 percent for the first and second data sets, respectively.

A major limitation to the use of wireless sensor networks (WSNs) in asset monitoring applications is security and privacy, particularly the privacy of source location information. In this article, we develop two phantom routing-based solutions to provide source location privacy for the case of multisource/asset scenario—the case that has received very little attention in the literature. The idea of phantom routing is to relay the packets to a distant node in a random fashion to obfuscate the traffic flows and confuse the attacker. The first technique phantom routing-based backward random walk (PRBRW) uses a combination of backward random walk (RW) and a greedy forwarding approach to route the packets to the base station (BS). Although the first approach has better performance improvements in terms of capture ratio and safety period it hampers the lifetime of the network and has a poor entropy metric. To better this problem, an improved phantom routing scheme phantom routing-based L-path RW (PRLPRW) is proposed. The second technique has three phases: 1) pure RW; 2) L-walk; and 3) greedy walk. This technique performs well in terms of capture ratio, safety period, and entropy metrics. The improvement in network lifetime is 10-folds and entropy is 477-folds when compared with PRBRW. The performance is evaluated using the developed analytical models and compared with the baseline protection-less scheme shortest path routing (SPR). It is observed that PRBRW and PRLPRW, respectively, have 60- and 73-fold improvements in terms of capture ratio when compared with SPR, whereas existing phantom routing-based pure RW and forward RW techniques, respectively, have only 54- and 34-fold improvements.

Long distance communication links may severely affect the cyber-physical systems (CPSs) in 5G (and future 6G) networks and degrade its reliability and resilience by disrupting the quality index of network latency. Further, centralized network architectures have low fault tolerance and are prone to security threats. Virtualized software defined network (vSDN)-enabled 5G networks closely monitor these facts and redefine the existing network topology to find potential locations for deploying controller and hypervisor instances. In this article, we propose an approach of dynamically deploying controller-hypervisor (C-H) pair(s) to provide a variety of network functions like differentiation between control and data signals, various translation functions, etc., with ultra low latency (ULL). The system model deals with real network topology and four well-defined network latency matrices with a mixed integer linear programming model to optimize latency objectives. A reverse path-flow mechanism (RPFM) has been proposed to provide feasible solutions by keeping the network load, and controller capacity under a tolerance limit. We have further minimized the H-plane load by distributing the network resources based on the arrival time of SERVICE_IN requests from the users. Simulation results show that our proposed technique achieves significant reduction in latency and an evolved-ULL (e-ULL) experience, where all real-time user demands are handled efficiently. The proposed approach can also be used for similar critical localization problems like service chain mapping in 5G-NR, baseband unit deployment in 5G C-RAN and firewall deployment in distributed CPS.

A wireless sensor network consists of a large number of low-powered, short-lived, unreliable sensors. One major challenge is to extend the network lifetime as well as ensure network coverage. In this paper, we present a novel probabilistic method for node scheduling with the goal of increasing network lifetime while maintaining network coverage. The method exploits redundancy of sensor nodes inherent in a dense sensor network while maintaining low communication overhead. We also introduce the concept of variable coverage level to cater to different coverage requirements of varied application scenarios. Simulation results show the validity of the proposed approach.

With device-to-device (D2D) based relay-assisted communications, deep faded cellular users (CUs) get a better connectivity. The deep faded CU's achievable throughput of the link from base station (BS) to the CU is less than the rate demand from the CU (unsatisfied CUs). In this paper, we study the resource allocation problem for both CUs and D2D pairs to increase the achievable throughput. This is accomplished by reducing the difference between rate demand and rate achieved for the active CUs. The resource allocation optimization problem is solved in three steps. The initial set of resources that can be allocated to each active CUs is determined in the first step. In the second step, it finds the set of active CUs which needs D2D assistance and then it finds suitable D2D relay nodes (DRNs). Resource allocation to those DRNs and D2D pairs without degrading the link throughput of active CUs is performed in the third step. Numerical results shows significant amount of throughput gain for the active CUs using D2D communication. We also measure the performance of our proposed model in terms of churn rate and the number of excess resource blocks (RBs) in the system.

Railway Bridge Health Monitoring is of prime importance as damages in bridges can lead to heavy casualties. Hence, monitoring is necessary to provide safety services to the millions of people around the world. Recently, wireless sensor network (WSN) has come up as a promising technology for health monitoring. However, memory and energy constraints of WSN, and extracting intelligible information from signals obtained from complex bridge structures are the technical challenges in this domain. In this paper, we give a brief overview of a novel railway bridge health monitoring system using smart WSN.

In this paper, we propose a low-overhead identity-based distributed dynamic address configuration scheme for secure allocation of IP addresses to authorized nodes of a managed mobile ad hoc network. A new node will receive an IP address from an existing neighbor node. Thereafter, each node in a network is able to generate a set of unique IP addresses from its own IP address, which it can further assign to more new nodes. Due to lack of infrastructure, apart from security issues, such type of networks poses several design challenges such as high packet error rate, network partitioning, and network merging. Our proposed protocol takes care of these issues incurring less overhead as it does not require any message flooding mechanism over the entire MANET. Performance analysis and simulation results show that even with added security mechanisms, our proposed protocol outperforms similar existing protocols.

In this paper, we propose a game theoretic model for zero-sum stochastic routing game in presence of the intelligent attackers in mobile ad hoc networks (MANETs). In each and every stage of the game, the source node observes the available multiple paths, the residual bandwidth of the paths, quality of the paths, and the strategy of the attackers from the previous stage of the game. Based on this observation, the source node selects an optimal routing strategy by using minimax-Q learning for secure multihop data flow from source to destination through a attack free path among the multiple paths. The selected optimal routing strategy of multihop data flow maximizes the expected payoff defined as the bandwidth utilization. The proposed technique achieves significant performance gains in terms of bandwidth utilization in MANETs. It also provides a randomized security of multihop data flow by accommodating the stochastic multihop data flow, the system dynamics, and the strategic behaviors of the attackers.

Secure distributed IP addressing is a prime requirement in mobile ad hoc networks (MANETs) for unicast communication. Several types of security threats have been observed in most of the proposed dynamic addressing approaches as they rely on broadcasting for address solicitation and/ or duplicate address detection. In this paper, we propose an ID based distributed dynamic IP configuration scheme to securely allocate IP addresses to the authorized hosts for a MANET without broadcasting over the entire network. Each host can generate unique IP addresses from its own IP address and can assign those addresses to the new nodes. In addition, we propose a novel signature scheme that authenticates and lessens the security threats associated with dynamic IP configuration. Proof of correctness of the proposed signature scheme verifies that the scheme is secure against any forgery attack. Extensive simulation results show that the proposed addressing scheme has low overhead and fairly good addressing latency with added security mechanisms as compared to similar existing dynamic configuration schemes. Moreover, the proposed scheme is robust and can efficiently solve the problem of network partitions and mergers.

Mobile ad hoc networks (MANETs) were originally designed for a cooperative environment. To use them in hostile environments, trust-based routing can be used, where instead of establishing the shortest routes as done in traditional routing protocols, most trusted routes are established. In this study, the authors present a light-weight trust-based routing protocol. It is light-weight in the sense that the intrusion detection system (IDS) used for estimating the trust that one node has for another, consumes limited computational resource. Moreover, it uses only local information thereby ensuring scalability. Our light-weight IDS takes care of two kinds of attacks, namely, the blackhole attack and the grey hole attack. Whereas our proposed approach can be incorporated in any routing protocol, the authors have used AODV as the base routing protocol to evaluate our proposed approach and give a performance analysis.

In IEEE 802.11 standard MAC layer and Physical layer overheads are necessary for the proper synchronization of transmitter and receiver besides sharing the wireless medium efficiently. To increase the network efficiency, the ratio of header size to payload size in a packet has to be reduced. The new IEEE WLAN amendment, IEEE 802.11n, allows aggregation of packets to increase the payload size. Here a node aggregates the packets of different applications to compose a larger packet before it is send to the access point (AP). However, this method fails in the case of delay sensitive multimedia applications. In this paper, we propose three methods that use packet aggregation to improve the system throughput of real time multimedia applications in a WLAN. These schemes use capture effect, power control scheme and directionality of antenna to allow concurrent packet transmissions in a WLAN. Our simulation studies show that these schemes increase the system throughput considerably.

In this paper, we present a polynomial time algorithm that gives an optimal solution to the routing and wavelength assignment (RWA) problem in a tree topology. One of the major design issues in wavelength-division multiplexed networks is the assignment of the limited number of wavelengths among network stations so that greater capacity can be achieved. The problem of RWA is known to be NP-hard problem. Many researchers have tackled the problem of RWA with a number of efficient heuristic algorithms. This paper presents an algorithm that optimally assigns a single wavelength to maximize one-hop traffic in a tree topology. The algorithm uses dynamic programming and is shown to be optimal with a time complexity of O(N/sup 4/). We also propose a heuristic scheme to use our optimal algorithm for wavelength assignment in a general graph. The heuristic works on the tree subgraphs of a given graph and the remaining spare wavelengths can be assigned with an existing RWA policy.