The rapid increase in road traffic has significantly contributed to urban air pollution, excessive fuel consumption, and toxic emissions, necessitating sustainable mobility solutions. This paper introduces the Sustainable Route Recommendation System (SRRS), a smart system that helps find the best driving routes to reduce carbon dioxide (CO2) and nitrogen oxide (NOx) emissions, save fuel, and shorten travel time. SRRS combines two important algorithms: the first is a YOLO-based model that quickly identifies and sorts vehicles into heavy and light types, and the second is a SUMO-based traffic simulation that suggests the best routes based on the type of vehicle. By intelligently distributing traffic, the system reduces congestion and enhances sustainability. Simulations at Apex Circle, Jaipur, show that SRRS reduces CO2emissions by 30.05%, NOxemissions by 31.97%, and fuel consumption by 30.2% compared to conventional methods. These findings establish SRRS as a promising solution for sustainable urban mobility, reducing environmental impact while improving traffic efficiency.

The beyond-diagonal reconfigurable intelligent surface (BD-RIS) represents the next advancement in reconfigurable intelligent surfaces (RIS), offering enhanced flexibility by supporting multiple modes and architectures. This study examines the spectral efficiency (SE) of a single-cell RSMA-based multi-user MIMO system assisted by two sector BD-RIS. The scattering matrix of BD-RIS and the power allocation between common and private precoders primarily determine the SE. We employ a two-step optimization process to determine these parameters. First, the scattering matrix is optimized by maximizing the total channel gain of the user equipment (UEs). Using the optimized scattering matrix, the power allocation for the common and private streams is then calculated by maximizing the SE of the system through a fractional programming technique, which gives a closed-form solution. The SE is computed using the optimized parameters and is numerically plotted to evaluate the performance of the system. The system is extensively analyzed across various precoding techniques, numbers of UEs, and BD-RIS elements, and is also compared to space division multiple access (SDMA). Numerical results show that BD-RIS outperforms both the traditional RIS and the scenario without RIS.

Internet of Things (IoT) is envisioned as a large collection of smart devices that are connected to the Internet and communicate with the goal of realizing a diverse range of applications. These smart devices range from typical home appliances to sophisticated industrial instruments. IoT has numerous applications, such as precision farming, health care, and smart cities. An issue that is prevalent in IoT networks is that due to limited resources, environmental factors, and malicious attacks, some nodes or links fail and adversely affect the functioning of IoT networks. Hence, robustness against the failure of nodes or links is a topic of considerable interest in the area of IoT networks. The robustness of a network is quantified using various spectral graph theoretic measures in network science. One such measure is network criticality which effectively quantifies the robustness against the failure of nodes or communication links. However, this measure can not be used to study the effect of different network parameters for large-scale IoT networks due to huge computational complexity of$O(n^{3})$. In this work, we derive the explicit formulas of network criticality for IoT networks using$r$-nearest neighbor graphs and show the effect of nearest neighbors and network size on robustness. Our theoretical expressions substantially reduce the computational complexity as compared to existing graph theory-based metrics. We observe that network robustness decreases with the network size and exponentially increases with nearest neighbors. Our work reduces the time complexity of network criticality evaluation from$O(n^{3})$to$O(n)$for static topologies and to$O(1)$for switching topologies. Furthermore, we extend our study to random geometric graphs (RGGs) and real-world network data sets. Finally, we study the effect of asymmetric and dynamic topologies on robust IoT networks.

To mitigate the attacks by malicious peers and to isolate them in peer-to-peer (P2P) networks, several reputation systems have been proposed in the past. The relative ranking method is the most popular among them. In this method, peers are ranked according to their reputation in the network. The major limitation of this method is that it does not give any absolute interpretation about the peers, it can only differentiate among the given set of peers. This is more significant when all the peers responding to a query, are malicious. In such a situation, we can only know that who is better among them without knowing their actual reputation in the whole network. In this article, we are proposing a new algorithm which can rank the peers in the whole network as well as can give an absolute interpretation of their reputation. Consequently, we can identify them as good peers or malicious peers. Further, by choosing the suitable values of parameters, our algorithm converges much faster at some global consensus. Simulation results show that it performs better in load balancing and in reducing the inauthentic downloads in the network as compared to the existing algorithms.

In a peer-to-peer system, a node should estimate reputation of other peers not only on the basis of its own interaction, but also on the basis of experience of other nodes. Reputation aggregation mechanism implements strategy for achieving this. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours' direct interaction with the node. Theoretical analysis and numerical results show that differential gossip is fast and requires lesser amount of resources. The reputation computed using the proposed algorithm also shows a good amount of immunity to the collusion.

In a peer-to-peer system, a node should estimate reputation of other peers not only on the basis of its own interaction, but also on the basis of expression of other nodes. Reputation aggregation mechanism implements strategy for achieving this. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, most of the methods consider that a node will have the same reputation after aggregation with all the nodes in the network, which is not true. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours' direct interaction with the node. The differential gossip is fast and requires a lesser amount of resources. This mechanism allows computation of independent reputation value by every node, of every other node in the network. The differential gossip trust has been investigated for a power law network formed using preferential attachment (PA) Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion. We have verified the performance of the algorithm on the power law networks with sizes ranging from 100 nodes to 50,000 nodes.

A new class of optical cross-connects (OXCs) is being proposed. These OXCs are highly scalable and possess the flexibility of 3-stage Clos networks so that various non-blocking properties, viz., rearrangeably nonblocking and strictly nonblocking (SNB) can be obtained from the same architecture. They are well-suited to implement SNB OXCs with high values of wavelength to fiber ratio, require lesser number of blocks than Clos network and support both fiber and wavelength scalability. The OXCs when implemented in three-dimensional micro-opto-electro-mechanical systems (3D MOEMS) technology provide greater advantages. A specific instance of the class of architectures is suitable for limited-range wavelength converters.

We consider WDM networks with lightpath switching where wavelengths may be converted, as required, along the lightpath. For efficient converter usage in such a network, sparse converter placement may be followed where only some of the network nodes are equipped with wavelength converters. Given the nominal network traffic pattern, we present a simple heuristic algorithm which may be used to determine the location of these converters for good network performance. For a network designed in this fashion, we consider the application of a path-metric based heuristic algorithm for lightpath routing and wavelength selection along the links of the selected route. Dynamic operation of the sparse converter network is considered using this path-metric based algorithm for lightpath routing and wavelength selection.