The Low-Altitude Intelligent Network (LAIN) serves as a critical enabler for low-altitude economic development, showing significant potential in areas such as environmental monitoring. These applications typically require extensive computational resources to drive large-scale models, posing challenges to resource-constrained aerial platforms like unmanned aerial vehicles (UAVs). Although air-ground model collaboration has been studied by some early studies, most approaches assume homogeneous computational resources, overlooking the heterogeneity among UAVs as end nodes. To address this challenge, we introduce an air-ground model collaboration framework that considers heterogeneous computational resources among multiple UAVs and limited wireless transmission bandwidth between UAVs and ground servers. In this framework, UAVs working as end nodes handle data collection and local inference with small models, while the edge servers on the ground perform large model inference and updates. We propose a joint optimization strategy that optimizes both data transmission and resource allocation, with the goal of improving the framework’s inference accuracy by maximizing the mean average precision (mAP). Simulations on object detection tasks show that our framework outperforms existing methods under different communication bandwidths and data scales.

In the traditional index coding problem, a server employs coding to send messages to a set of clients within the same broadcast domain. Each client already has some messages as side information and requests a particular unknown message from the server. All clients learn the coding matrix so that they can decode and retrieve their requested data. Our starting observation comes from the work by Karmoose et al., which shows that learning the coding matrix can pose privacy concerns: it may enable a client to infer information about the requests and side information of other clients. In this paper, we mitigate this privacy concern by allowing each client to have limited access to the coding matrix. In particular, we design coding matrices so that each client needs only to learn some of (and not all) the rows to decode her requested message. We start by showing that this approach can indeed help mitigate that privacy concern. We do so by considering two different privacy metrics. The first one shows the attained privacy benefits based on a geometric interpretation of the problem. Differently, the second metric, referred to as maximal information leakage, provides upper bounds on: (i) the guessing power of the adversaries (i.e., curious clients) when our proposed approach is employed, and (ii) the effect of decreasing the number of accessible rows on the attained privacy. Then, we propose the use of k-limited-access schemes: given an index coding scheme that employs T transmissions, we create a k-limited-access scheme with Tk≥ T transmissions, and with the property that each client needs at most k transmissions to decode her message. We derive upper and lower bounds on Tkfor all values of k, and develop deterministic designs for these schemes, which are universal, i.e., independent of the coding matrix. We show that our schemes are order-optimal for some parameter regimes, and we propose heuristics that complement the universal schemes for the remaining regimes.

It was recently observed in [1], that in index coding, learning the coding matrix used by the server can pose privacy concerns: curious clients can extract information about the requests and side information of other clients. One approach to mitigate such concerns is the use of k-limited-access schemes [1], that restrict each client to learn only part of the index coding matrix, and in particular, at most k rows. These schemes transform a linear index coding matrix of rank T to an alternate one, such that each client needs to learn at most k of the coding matrix rows to decode its requested message. This paper analyzes k-limited-access schemes. First, a worst-case scenario, where the total number of clients n is 2T-1 is studied. For this case, a novel construction of the coding matrix is provided and shown to be order-optimal in the number of transmissions. Then, the case of a general n is considered and two different schemes are designed and analytically and numerically assessed in their performance. It is shown that these schemes perform better than the one designed for the case n=2T-1.

Social groups open up the opportunity for a new form of caching. This paper investigates how a social group of users can jointly optimize bandwidth usage, by each caching network-coded parts of the data demand, and then opportunistically share these parts among themselves upon meeting. First, the problem is formulated as a linear program (LP) with exponential complexity in the number of users. Then, a heuristic algorithm is proposed, which is inspired by the bipartite set-cover problem and operates in polynomial time. For some scenarios, a worst-case performance guarantee of the heuristic with respect to the optimal LP solution is proved. Finally, the performance of the algorithm is assessed using real-world mobility traces synthesized using the SWIM model and from the MIT Reality Mining project data set. The proposed heuristic offers bandwidth savings up to 65% for a waiting time of 30 minutes and up to 28% performance gains with respect to the alternative solutions. These benefits make the algorithm a feasible candidate solution for bandwidth savings.

Using a broadcast channel to transmit clients' data requests may impose privacy risks. In this paper, we tackle such privacy concerns in the index coding framework. We show how a curious client can infer some information about the requests and side information of other clients by learning the encoding matrix used by the server. We propose an information-theoretic metric to measure the level of privacy and show how encoding matrices can be designed to achieve specific privacy guarantees. We then consider a special scenario for which we design a transmission scheme and derive the achieved levels of privacy in closed-form. We also derive upper bounds and we compare them to the levels of privacy achieved by our scheme, highlighting that an inherent trade-off exists between protecting privacy of the request and of the side information of the clients.

In this paper, we revisit the communication vs. distributed computing trade-off, studied within the framework of MapReduce in [1]. An implicit assumption in the aforementioned work is that each server performs all possible computations on all the files stored in its memory. Our starting observation is that, if servers can compute only the intermediate values they need, then storage constraints do not directly imply computation constraints. We examine how this affects the communication-computation trade-off and suggest that the trade-off be studied with a predetermined storage constraint. We then proceed to examine the case where servers need to perform computationally intensive tasks, and may not have sufficient time to perform all computations required by the scheme in [1]. Given a threshold that limits the computational load, we derive a lower bound on the associated communication load, and propose a heuristic scheme that achieves in some cases the lower bound.

Index coding employs coding across clients within the same broadcast domain. This typically assumes that all clients learn the coding matrix so that they can decode and retrieve their requested data. However, learning the coding matrix can pose privacy concerns: it may enable clients to infer information about the requests and side information of other clients [1]. In this paper, we formalize the intuition that the achieved privacy can increase by decreasing the number of rows of the coding matrix that a client learns. Based on this, we propose the use of k-limited-access schemes: given an index coding scheme that employs T transmissions, we create a k-limited-access scheme with Tk≤ T transmissions, and with the property that each client learns at most k rows of the coding matrix to decode its message. We derive upper and lower bounds on Tkfor all values of k, and develop deterministic designs for these schemes for which Tkhas an order-optimal exponent for some regimes.

Social groups give the opportunity for a new form of caching. In this paper, we investigate how a social group of users can jointly optimize bandwidth usage, by each caching parts of the data demand, and then opportunistically share these parts among them upon meeting. We formulate this problem as a Linear Program (LP) with exponential complexity. Based on the optimal solution, we propose a simple heuristic inspired by the bipartite set-cover problem that operates in polynomial time. Furthermore, we prove a worst case gap between the heuristic and the LP solutions. Finally, we assess the performance of our algorithm using real-world mobility traces from the MIT Reality Mining project dataset.

Censoring has been proposed to be utilized in wireless distributed detection networks with a fusion center to enhance network performance in terms of error probability in addition to the well- established energy saving gains. In this paper, we further examine the employment of censoring in infrastructure-less cognitive radio networks, where nodes employ binary consensus algorithms to take global decisions regarding a binary hypothesis test without a fusion center to coordinate such a process. We show analytically - and verify by simulations - that censoring enhances the performance of such networks in terms of error probability and convergence times. Our protocol shows performance gains up to 46.6\% in terms of average error probability over its conventional counterpart, in addition to performance gains of about 48.7% in terms of average energy expenditure and savings up to 50% in incurred transmission overhead.

In this paper, we consider a sequential binary hypothesis testing framework in wireless sensor networks. We study the effect of sensor censoring on network performance in terms of the average error probability and average number of observations required until a global decision is made. The detection process is mathematically modeled as a random walk process with two absorbing barriers. We resort to Chernoff bound in order to find upper bounds on the error probabilities and the average stopping time. The main contribution of this paper is to prove that in a sequential binary hypothesis network where sensors send their hard decisions to the fusion center, censoring can enhance the network performance in comparison to non-censoring networks in certain SNR regimes. Numerical evaluation is provided to illustrate the gains achieved through censoring.

We consider a cognitive radio setting in which a relay-assisted secondary link employs cooperative beamforming to enhance its throughput and to provide protection to the primary receiver from interference. We assume the presence of infinite buffers at both the primary and secondary transmitters and characterize the maximum stable throughput region exactly using the dominant system approach. Numerical examples are provided to give insights into the impact of power control on the stability region.

Current routing protocols for cognitive radio networks are severely affected by the frequent activity of primary users. Nodes that are in the interference range of an appearing primary user are not allowed to transmit, and therefore existing routes which utilize such nodes are obliged to undergo a route maintenance phase. This naturally provides other routes to the destination that may incur extra delay or increase packet queuing overhead. In this work, a novel route maintenance protocol is proposed that allows existing routes to endure the event of primary user presence by forming cooperative links between neighboring nodes and nulling out transmission at the primary receiver using cooperative beamforming. Our proposed protocol can be used in conjunction with any of the existing routing protocols, thus achieving modularity. Extensive simulations are done which prove that our proposed protocol outperforms existing route maintenance techniques in terms of end-to-end delay and loss ratio, with minimal incurred overhead.

In this paper, we consider binary hypothesis testing for distributed detection in Wireless Sensor Networks. Sensor nodes individually take a decision upon which hypothesis is currently present. Communication between sensor nodes and the Fusion Center is done through a Type-Based Multiple Access (TBMA) scheme, and the Fusion Center gives a global decision about the hypothesis under consideration. We consider the case where each sensor has the ability to "censor" transmission, meaning that a sensor node can locally withhold transmission if local observation is unreliable. The major contribution in this paper is to show that for the TBMA scheme with sensors sending binary decisions to the Fusion Center, censoring can achieve lower probability of decision error even if sufficient energy and/or rate of transmission is available.