Power saving in wireless ad hoc networks without synchronization

Power saving strategies generally attempt to maximize the time that nodes spend in a low power consumption sleep state. Such strategies often require the sender to notify the receiver about pending traffic using some form of traffic announcement. Although asynchronous traffic announcement mechanisms are particularly suitable for the ad hoc environment, they also provide relatively limited power savings. This paper proposes a mechanism that improves the efficiency of asynchronous traffic announcement mechanisms by reducing the proportion of time that nodes need to spend awake, while still maintaining good connectivity properties. The mechanism is based on allowing traffic announcements to be rebroadcast by neighbouring nodes

A topology-oblivious routing protocol for NDN-VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low

On-demand Construction of Non-interfering Multiple Paths in Wireless Sensor Networks

In this paper we present a routing scheme for on-demand construction of multiple non-interfering paths in wireless sensor networks. One usage of this multipath scheme is to provide a source the ability to increase the likelihood that its data reaches the sink by sending a copy of a packet on more than one path. The routing scheme is based on the assumption that the sensor nodes are aware of their geographic position

Edge Provisioning and Fairness in VPN-DiffServ Networks

Customers of Virtual Private Networks (VPNs) over Differentiated Services (DiffServ) infrastructure are most likely to demand not only security but also guaranteed Quality-of-Service (QoS) in pursuance of their desire to have leased-line-like services. However, expectedly they will be unable or unwilling to predict the load between VPN endpoints. This paper proposes that customers specify their requirements as a range of quantitative services in the Service Level Agreements (SLAs). To support such services Internet Service Providers (ISPs) would need an automated provisioning system that can logically partition the capacity at the edges to various classes (or groups) of VPN connections and manage them efficiently to allow resource sharing among the groups in a dynamic and fair manner. While with edge provisioning a certain amount of resources based on SLAs (traffic contract at edge) are allocated to VPN connections, we also need to provision the interior nodes of a transit network to meet the assurances offered at the boundaries of the network. We, therefore, propose a two-layered model to provision such VPN-DiffServ networks where the top layer is responsible for edge provisioning, and drives the lower layer in charge of interior resource provisioning with the help of a Bandwidth Broker (BB). Various algorithms with examples and analyses are presented to provision and allocate resources dynamically at the edges for VPN connections. We have developed a prototype BB performing the required provisioning and connection admissio

Dynamic Federated Learning for Heterogeneous Learning Environments

The emergence of the Internet of Things (IoT) has resulted in a massive influx of data generated by various edge devices. Machine learning models trained on this data can provide valuable insights and predictions, leading to better decision-making and intelligent applications. Federated Learning (FL) is a distributed learning paradigm that enables remote devices to collaboratively train models without sharing sensitive data, thus preserving user privacy and reducing communication overhead. However, despite recent breakthroughs in FL, the heterogeneous learning environments significantly limit its performance and hinder its real-world applications. The heterogeneous learning environment is mainly embodied in two aspects. Firstly, the statistically heterogeneous (usually non-independent identically distributed) data from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing studies address only the unilateral side of the heterogeneity issue, either the statistical or the resource heterogeneity. However, the resource heterogeneity among various devices does not necessarily correlate with the distribution of their training data. We propose Dynamic Federated Learning (DFL) to address the joint problem of data and resource heterogeneity in FL. DFL combines resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. Using resource-aware split learning, the allocation of the FL training tasks on resource-constrained participants is adjusted to match their heterogeneous computing capabilities, while resource-capable participants carry out the classic FL training. We employ centered kernel alignment for determining the similarity of neural network layers to address the data heterogeneity and carry out layerwise sub-model aggregation. Preliminary results indicate that the proposed technique can improve training performance (i.e., training time, accuracy, and energy consumption) in heterogeneous learning environments with both data and resource heterogeneity

Attention-based Neural Networks for Multi-modal Trajectory Prediction

Trajectory prediction is of great importance in wireless and intelligent networks. Accurate forecast of users’ trajectories can provide efficient handover management, continuous network connection, and generally better network quality of service. A trajectory is defined as the sequence of location logs, e.g., GPS coordinates or cellular antenna IDs, over time. We present a trajectory predictor based on Transformers Neural Networks acquiring the self-attention mechanism [1]. Mobile objects’ mobility patterns are influenced by their nearby neighbors. Thus, learning spatio-temporal dependencies among neighbor-trajectory users can help to better predict their trajectories [2]. In this direction, unlike our previously proposed mobility predictor (based on LSTM and CNN) designed for single agents [3], [4], [5], where agents were acting in isolation, we now propose the INteractive TRAnsformers ReinFORCEd (INTRAFORCE) social-aware neural network. We further employ a reinforcement learning agent to design the highest-performance transformers neural architecture based on the multi-modal trajectory scenario. Evaluations show that using the Orange dataset [4], our transformer-based predictor can remarkably increase the accuracy and decrease the training time and computations concerning our models based on LSTM and CNN [4]. Furthermore, on ETH+UCY datasets [6], INTRAFORCE achieves the least Mean Square Error compared to numerous state-of-the-art mechanisms on this popular dataset