Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning

Anthony Tzes; Michalis Smyrnakis; Sotiris Papatheodorou; Tembine Hamidou

REVIEW 3 major objections 4 minor 18 references

Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A cooperative-game reformulation lets teams of data-mule robots schedule tours with simple learning rules, trading guaranteed optimality for a roughly tenfold speedup over MILP.

desk verdict A plausible heuristic for MES scheduling is undercut by a false potential-game claim and very thin simulation evidence. read the letter →

arxiv 2411.14619 v1 pith:KTL5EPYG submitted 2024-11-21 eess.SY cs.SY

Sotiris Papatheodorou , Michalis Smyrnakis , Tembine Hamidou , Anthony Tzes This is my paper · ORCID

classification eess.SYcs.SY

keywords RoboticDataMuleWirelessSensorNetworksMulti-AgentSystemsDistributedOptimizationGame-TheoreticLearningMobileElementSchedulingEnergy-EfficientPathPlanning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the computationally hard problem of scheduling data-mule robots to visit wireless sensor nodes can be replaced by a cooperative game that robots solve with simple iterative learning rules. Instead of solving the NP-hard Mobile Element Scheduling MILP, the planner assigns a reward to each possible robot tour based on its energy cost and on whether it overlaps with other robots' tours, and the robots update their choices until they reach a mutually acceptable set of tours. The authors claim this game is a potential game, which is what gives the learning rules their convergence rationale, and their simulations show acceptable tours emerging in tens of iterations at about a tenth of the MILP solver's runtime. A reader should care because the trade-off—near-real-time computation at the price of suboptimal tours that were 22–25% above best in the test cases—could make data-mule scheduling practical in settings where the optimal solver is too slow.

What carries the argument

The load-bearing object is the per-robot reward $r_i(s_i,s_{-i})$ in equation (4). If the sensors $a_i$ that robot $i$ plans to visit are disjoint from every other robot's planned sensors $a_{-i}$, robot $i$ receives $1/(c_i P_i)$, where $c_i$ is the energy cost of its chosen tour and $P_i$ is the maximum energy cost over all of robot $i$'s allowed tours; if its sensor set overlaps another robot's, it receives $-c_i$. The paper asserts that the resulting game is a potential game with potential $\varphi = \sum_{i=1}^N r_i(s_i,s_{-i})$, so a unilateral improvement by one robot moves the team-wide sum in the same direction, aligning individual learning with the team objective. A joint action is acceptable only when every sensor is visited by exactly one robot, and the shared reward is a large penalty $-C$ for unacceptable joint actions and $\varphi$ otherwise. The learning algorithms update each robot's route choice from its own reward history, and the potential-game convergence properties are the theoretical reason to expect them to settle on a pure Nash equilibrium.

What would settle it

The concrete check is to enumerate all joint actions in the paper's 3-robot/4-sensor setup, evaluate equation (4) for every unilateral route change by one robot with the others fixed, and test whether $r_i(s_i,s_{-i}) - r_i(\tilde s_i,s_{-i}) = \varphi(s_i,s_{-i}) - \varphi(\tilde s_i,s_{-i})$ holds in every case; a single counterexample would falsify the potential-game claim that all the convergence guarantees rest on.

Watch

Extended reading notes

Core claim

The paper's central claim is that energy-aware data-mule scheduling, classically a Mixed-Integer Linear Programming formulation of the NP-hard Mobile Element Scheduling / subset-tour problem, can be recast as a finite cooperative game and solved by game-theoretic learning rules. Each robot's actions are candidate tours (subsets of up to $M_{\max}$ sensors in a chosen order), and the payoff of equation (4) rewards a tour when its sensor set does not overlap any other robot's tour, with magnitude tied to the inverse of the tour's energy cost, and penalizes an overlapping tour with its full energy cost. The authors assert that this game is a potential game—more than one pure Nash equilibrium exists—so a coordination mechanism is needed, and that the standard learning rules (best response, fictitious play, geometric fictitious play, and joint-strategy fictitious play with inertia) can serve as that mechanism. Their simulations with three robots and four sensors show acceptable joint tours emerging in tens of iterations, with joint-strategy fictitious play reaching costs 25.3% and 22.12% above the best-known solutions in the two scenarios while delivering a typical tenfold reduction in computation time relative to a MILP solver.

Load-bearing premise

The approach stands or falls on the claim that when one robot changes its planned route, the change in the team's total reward is exactly mirrored by the change in that robot's own reward; this is asserted by reference to another paper rather than proved, and all the convergence guarantees rely on it.

Editorial extensions

If this is right

If the reformulation holds, mobile-element scheduling can be run as an online distributed process: each robot updates only its own route preferences, and acceptable joint tours emerge in tens of iterations rather than from a centralized MILP solve.
The reported order-of-magnitude speedup means the approach could be used where MILP is too slow for near-real-time replanning, at the cost of tours that in the test cases were 22–25% above the best-known energy cost.
Because the convergence guarantees are generic for potential games, new learning rules with better equilibrium selection could be dropped into the same utility formulation without changing the path-energy computation.
The decomposition of the robot team into communication-connected subgroups with generalized Voronoi regions of responsibility bounds each sub-game's action space, which is what makes the approach scalable to larger networks.
In the tested scenarios, best response sometimes failed to reach an acceptable action, so the choice of learning rule matters for reliability, not only for speed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An online re-planning variant is a natural next step: with geometric fictitious play's discounted averaging, the same utility could be re-solved as sensor buffers fill or batteries drain, though the paper only treats the static assignment.
The binary overlap penalty is the most adjustable part of the design; replacing it with a graded congestion cost could push equilibria closer to the MILP optimum while preserving the potential structure, which is a direct testable modification.
The practical claim should be stress-tested at larger problem sizes: the action space per robot grows combinatorially as $1+\sum_{k=1}^{M_{\max}} M!/(M-k)!$, so the near-real-time advantage in a 3-robot, 4-sensor scenario may not transfer without the Voronoi decomposition doing most of the work.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The short version: the paper applies fictitious-play variants to an energy-aware task allocation problem for data-mule robots, and the idea is reasonable. But the theoretical justification—that the reward function (4) is a potential game—is wrong, and the simulations are a single toy run with no code. The paper is not ready for publication, but it's not worthless.

What's new: the utility (4) that sums per-route energy costs and penalizes overlap is a sensible way to convert the MES problem into a game. The formulation as a distributed learning problem is a legitimate angle that could have practical value, and the use of joint strategy fictitious play to avoid MILP is worth exploring. The writing is mostly clear.

The load-bearing claim in Section V is that (4) admits a potential φ = Σ r_i, with the proof omitted and deferred to [18]. That claim is false. Here's a simple counterexample: two players, two sensors A and B, with costs c_1(A)=1, c_1(B)=2, c_2(A)=3, c_2(B)=1. Joint action (A,B) gives r_1=1/2, r_2=1/3, φ=5/6. If player 1 deviates to B, the joint action is (B,B), which overlaps, so r_1=-2, r_2=-1, φ=-3. Player 1's own change is -2.5, but φ changes by -3.833. The exact potential identity fails. The reason is generic: a unilateral move by i changes the overlap status, and hence the payoff, of every other player whose chosen sensors overlap with i's new action, so the sum of others' rewards cannot cancel from the difference. This invalidates the convergence guarantees from [12] that the paper relies on. The paper explicitly cites those guarantees for potential games, so the theoretical foundation collapses.

The paper also claims to present a derivation of optimal velocity profiles but doesn't include it; the path planner is taken from [8] and described only at a high level. The simulations are a single run of the 3-robot, 4-sensor case, with no error bars, no repeated trials, no code or data, and the MILP comparison is a single sentence about a 'typical tenfold reduction' with no detail. That's not enough to support the practical-utility claim.

Who is this for? Someone working on game-theoretic heuristics for multi-robot task allocation might find the problem formulation useful as a starting point, and the simulation results are suggestive. But the paper's central theoretical claim is false, and the evidence is too thin to stand on. It could be revised into a decent heuristic paper if the authors either fix the potential claim (perhaps by using a different utility or proving a different property like ordinal potential) or drop the theoretical framing and position it purely as an empirical heuristic with proper experiments.

I would not cite it in its current form, and I wouldn't send it to a top journal without major revision. But it does deserve a serious referee: the problem is real, the idea has potential, and the flaw is checkable. A venue with a solid review process could help the authors either fix it or redirect it. So, if this crossed my desk as an editor, I would send it out, with the expectation that the referee will catch the potential-game issue and require substantial rework.

Referee Report

3 major / 4 minor

Summary. The paper considers the problem of planning energy-efficient tours for a team of mobile robots that serve as data mules in a wireless sensor network. The authors formulate the route-allocation problem as a cooperative game in which each robot's action is a sequence of up to Mmax sensor visits, and the utility of each robot is given by Eq. (4): a positive reward proportional to the reciprocal of the action's energy cost when the visited sensor sets do not overlap, and a negative cost when they do. The paper claims that this utility admits a potential function φ = Σ_i r_i, and therefore that fictitious-play-type learning algorithms converge to a Nash equilibrium. Four learning algorithms (best response, fictitious play, geometric fictitious play, and joint-strategy fictitious play with inertia) are compared in simulations with three robots and four sensors, and the authors report roughly a tenfold runtime reduction relative to a MILP solver. The central theoretical claim is the potential-game property; the simulations are meant to demonstrate the practical utility of the approach.

Significance. If the potential-game claim were correct, the paper would offer a meaningful heuristic for a hard combinatorial problem (MES/TSSP), with the potential-game structure providing convergence guarantees for distributed learning and a possible order-of-magnitude speedup over MILP. The paper also integrates a realistic energy model from [8] and provides reproducible-looking simulation studies. However, the cornerstone theoretical assertion—that the utility in Eq. (4) admits the stated potential—is not merely unproved; it is false, as shown by a simple two-player counterexample. Since all the invoked convergence theorems (fictitious play, geometric fictitious play) apply to potential games, the theoretical basis for the approach collapses. The empirical results remain as heuristics, but the paper's central claim, that a game-theoretic formulation with guaranteed convergence properties is being used, is not supported. The claimed tenfold speedup is also not substantiated by any MILP comparison details.

major comments (3)

[Section V, Eq. (4)] The assertion that the reward function in (4) admits a potential of the form φ = Σ_i r_i is incorrect. Consider two players. Player 1 can visit sensor A at cost 1 or sensor B at cost 2, so P_1 = 2. Player 2 can visit sensor B at cost 1 or sensor A at cost 3, so P_2 = 3. For the joint action s = (A,B), no sensors overlap, so r_1(s) = 1/(1·2) = 0.5, r_2(s) = 1/(1·3) = 1/3, and φ(s) = 5/6. If player 1 deviates to B, the joint action is s' = (B,B), which overlaps, so r_1(s') = -2, r_2(s') = -1, and φ(s') = -3. The change in player 1's own payoff is r_1(s') - r_1(s) = -2.5, while the change in the proposed potential is φ(s') - φ(s) = -3 - 5/6 ≈ -3.833. These are unequal, violating the exact potential identity. The same mechanism affects ordinal potentiality: a unilateral change by player i can change the overlap status, and thus the payoff, of any other player whose chosen sensor set intersects the new action. Therefore the game defined by (4) is not a potential game with the stated φ, and the convergence results for fictitious play and geometric fictitious play cited in Section IV-B do not apply. The omitted proof referenced to [18] cannot establish this claim because the claim is false as stated.
[Section VI, runtime claim] The statement that 'a typical tenfold reduction of time has been observed compared to the classical MILP solver' is not supported by any experimental detail. The paper gives no specification of the MILP solver, the problem instances (beyond three robots and four sensors), the hardware, or the wall-clock times for either approach. Without this information, the tenfold claim is not verifiable, and it is not possible to assess whether the comparison is fair or whether the heuristic's speed advantage extends beyond the toy instance size tested.
[Section V, global reward] The global reward r(s) = -C if s is not acceptable, and φ otherwise, is introduced but never used by any of the learning algorithms described in Section IV-B. The players in the simulations maximize the individual utilities r_i from (4), not the global reward. The relationship between the potential φ and the team objective (minimizing total energy while visiting all sensors) is therefore unclear, and the definition of the global reward does not contribute to the convergence analysis or to the algorithm design.

minor comments (4)

[Algorithm 1] In line 9, the update for r_i^{t+1}(s_i) uses r_i^{t+1}(s_i) on the right-hand side; this should likely be r_i^t(s_i). Similarly, in line 16 the softmax expression uses r_i^{t+1}(s_i) after the update, but the notation is inconsistent with the typical definition of joint-strategy fictitious play.
[Section VI, Figure 3] The caption of Figure 3 says 'Team's cost for the third case', but the accompanying text describes this as the second case (robots visiting up to three sensors). Please correct the caption.
[Throughout] There are several typographical and formatting issues, including 'Generalized V oronoi' and 'F ig.' in the text, and the production quality of the figures is low. These should be cleaned up.
[Abstract and Section I] The phrase 'converges fast for most practical reasons' is vague and not quantifiable; the paper would benefit from a precise statement about convergence times and the conditions under which they hold.

Circularity Check

1 steps flagged · score 4.0 of 10

The Section V potential-game claim is imported from a self-cited omitted proof and is load-bearing for the convergence guarantees, though the simulation comparisons are independent.

self citation load bearing [Section V (Utility Formulation), after Eq. (4)]
"The reward function in (4) admits a potential of the form φ = Σ_{i=1:m} r_i(s_i,s_-i). The proof of this statement is similar to the proof of Theorem one in [18] and therefore it is omitted."

The potential-game property is the bridge from utility (4) to the Section IV-B convergence theorems for fictitious play and geometric fictitious play, which are stated for potential games. The paper does not prove the property; it defers to [18], whose first author is also an author of the present paper. The conclusion that this problem can be cast as a potential game therefore rests on an unverified self-citation. The claimed φ = Σ_i r_i is not a direct consequence of (4): a unilateral deviation by player i changes the conflict status and hence the rewards of other players, so the exact-potential identity can fail.

full rationale

The empirical content is self-contained: Section VI computes costs from the energy model and compares learning algorithms, and the reported tenfold speedup over MILP is an observable simulation result, not a renamed input. The circularity is localized to the theoretical grounding: Section V's assertion that (4) is a potential game is the premise that licenses the cited fictitious-play convergence results, but the proof is omitted and attributed to prior work by a co-author of the present paper. Because the unverified potential claim carries the convergence guarantee while the simulations are independent, a moderate score of 4 is appropriate rather than a higher score reserved for derivations that are equivalent to their inputs by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard game-theory background, the imported car-like robot model and energy-optimal path planner from [8], and an unproved assertion that the new utility (4) is a potential game. The potential assertion is the only non-standard, load-bearing axiom; all other assumptions are borrowed from cited work.

assumptions (4)

ad hoc to paper The reward function in (4) admits a potential of the form φ = Σ r_i (proof omitted, 'similar to Theorem 1 in [18]').
Section V states this without a proof; the exact-potential condition is not obvious because a player's action changes other players' rewards through overlap, so this is a load-bearing unproved assumption.
domain assumption The energy model Ei = ∫(c1 a^2 + c2 v^2 + c3 v + c4) dt from [8] accurately describes the robots' energy consumption.
Section III-A adopts this model from Tokekar et al. [8] without validation; all tour costs derive from it.
domain assumption The energy-optimal path between two poses can be found by A* on a graph of discretized pose-velocity pairs with straight-line and circular-arc edges.
Section III-B describes this graph search but gives no proof of optimality or details of the discretization, and the claimed derivation of optimal velocity profiles is not presented.
standard math Fictitious play variants converge to Nash equilibria in potential games (Monderer and Shapley [12]).
Section IV-B cites [12] for convergence of fictitious play in potential games; the paper then assumes the same holds for joint strategy fictitious play with inertia, though no proof is cited for that variant.

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Pith. "Pith review of Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning." pith.science (2026). https://pith.science/paper/KTL5EPYG

@misc{pith2026241114619,
  author       = {Pith},
  title        = {Pith review of: Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KTL5EPYG}},
  note         = {Machine review of arXiv:2411.14619}
}

read the original abstract

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.

Figures

Figures reproduced from arXiv: 2411.14619 by the authors.

**Figure 2.** Team’s cost for the first case. action of the robots was computed according to the process described in Section III-A. In the first case robots could choose up to two sensors to visit and thus the action space of each robot consists of seventeen actions. A robot could choose either to take no action, or one of the four single sensors, or to choose any combination of two sensors. In this case the best possible alloca… view at source ↗

**Figure 3.** Team’s cost for the third case. T1 T2 T3 T4 A1 A2 A3 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Trajectories of the robots for the first case when [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 15 canonical work pages

[18]

Improving multi-robot coordination by game-theoretic learning algorithms,

M. Smyrnakis, H. Qu, and S. M. Veres, “Improving multi-robot coordination by game-theoretic learning algorithms,” International Journal on Artificial Intelligence Tools , vol. 27, no. 07, 2018

work page 2018
[12]

Potential games,

D. Monderer and L. S. Shapley, “Potential games,” Games and Economic Behavior, vol. 14, no. 1, pp. 124–143, 1996

1996
[8]

Energy-optimal trajectory plan- ning for car-like robots,

P. Tokekar, N. Karnad, and V . Isler, “Energy-optimal trajectory plan- ning for car-like robots,” Autonomous Robots, vol. 37, no. 3, pp. 279– 300, October 2014

work page 2014
[1]

Wireless sensor networks: A survey,

V . Potdar, A. Sharif, and E. Chang, “Wireless sensor networks: A survey,” in International Conference on Advanced Information Networking and Applications Workshops. Bradford, UK: IEEE, May 2009, pp. 636–641. T1 T2 T3 T4 A1 A2 A3 Fig. 5: Trajectories of the robots for the second case when Joint strategy fictitious play algorithm is used

work page 2009
[2]

Robotic data mules for collecting data over sparse sensor fields,

D. Bhadauria, O. Tekdas, and V . Isler, “Robotic data mules for collecting data over sparse sensor fields,” Journal of Field Robotics , vol. 28, no. 3, pp. 388–404, 2011

work page 2011
[3]

Mobile robot trajectory planning for large volume data-muling from wireless sensor nodes,

O. Tsilomitrou, A. Tzes, and S. Manesis, “Mobile robot trajectory planning for large volume data-muling from wireless sensor nodes,” in 25th Mediterranean Conference on Control and Automation (MED). Valletta, Malta: IEEE, July 2017, pp. 1005–1010

work page 2017
[4]

Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines,

A. A. Somasundara, A. Ramamoorthy, and M. B. Srivastava, “Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines,” in 25th IEEE International Real- Time Systems Symposium . IEEE, December 2004, pp. 296–305

work page 2004
[5]

D. L. Applegate, R. E. Bixby, V . Chvatal, and W. J. Cook, The traveling salesman problem: A computational study . Princeton University Press, 2011

work page 2011

Show all 18 references

[6]

Energy modeling of differential drive robots,

M. Wahab, F. Rios-Gutierrez, and A. E. Shahat, “Energy modeling of differential drive robots,” in SoutheastCon, April 2015, pp. 1–6

2015
[7]

Energy-efficient motion planning for mobile robots,

Y . Mei, Y .-H. Lu, Y . C. Hu, and C. G. Lee, “Energy-efficient motion planning for mobile robots,” in IEEE International Conference on Robotics and Automation (ICRA) . New Orleans, LA, USA: IEEE, April 2004, pp. 4344–4349

2004
[9]

Nearest neighbourhood op- erations with generalized Voronoi diagrams: A review,

A. Okabe, B. Boots, and K. Sugihara, “Nearest neighbourhood op- erations with generalized Voronoi diagrams: A review,” International Journal of Geographical Information Systems, vol. 8, no. 1, pp. 43–71, 1994

1994
[10]

Fudenberg and J

D. Fudenberg and J. Tirole, Game Theory . Cambridge, MA: MIT Press, 1991

1991
[11]

Equilibrium points in n-person games,

J. F. Nash, “Equilibrium points in n-person games,” Proceedings of the National Academy of Sciences , vol. 36, no. 1, pp. 48–49, 1950

1950
[13]

Fudenberg and D

D. Fudenberg and D. K. Levine, The theory of learning in games . MIT press, 1998

1998
[14]

On similarities between inference in game theory and machine learning,

I. Rezek, D. S. Leslie, S. Reece, S. J. Roberts, A. Rogers, R. K. Dash, and N. R. Jennings, “On similarities between inference in game theory and machine learning,” Journal of Artificial Intelligence Research , vol. 33, pp. 259–283, 2008

2008
[15]

On the convergence of learning process in a 2x2 non- zero-person game,

K. Miyasawa, “On the convergence of learning process in a 2x2 non- zero-person game,” 1961

1961
[16]

An iterative method of solving a game,

J. Robinson, “An iterative method of solving a game,” Annals of Mathematics, vol. 54, no. 2, pp. 296–301, 1951

1951
[17]

“evolutionary

J. Nachbar, ““evolutionary” selection dynamics in games: Convergence and limit properties,” International Journal of Game Theory , vol. 19, pp. 59–89, 1990

1990

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [18]

Improving multi-robot coordination by game-theoretic learning algorithms,

M. Smyrnakis, H. Qu, and S. M. Veres, “Improving multi-robot coordination by game-theoretic learning algorithms,” International Journal on Artificial Intelligence Tools , vol. 27, no. 07, 2018

work page 2018

[2] [12]

Potential games,

D. Monderer and L. S. Shapley, “Potential games,” Games and Economic Behavior, vol. 14, no. 1, pp. 124–143, 1996

1996

[3] [8]

Energy-optimal trajectory plan- ning for car-like robots,

P. Tokekar, N. Karnad, and V . Isler, “Energy-optimal trajectory plan- ning for car-like robots,” Autonomous Robots, vol. 37, no. 3, pp. 279– 300, October 2014

work page 2014

[4] [1]

Wireless sensor networks: A survey,

V . Potdar, A. Sharif, and E. Chang, “Wireless sensor networks: A survey,” in International Conference on Advanced Information Networking and Applications Workshops. Bradford, UK: IEEE, May 2009, pp. 636–641. T1 T2 T3 T4 A1 A2 A3 Fig. 5: Trajectories of the robots for the second case when Joint strategy fictitious play algorithm is used

work page 2009

[5] [2]

Robotic data mules for collecting data over sparse sensor fields,

D. Bhadauria, O. Tekdas, and V . Isler, “Robotic data mules for collecting data over sparse sensor fields,” Journal of Field Robotics , vol. 28, no. 3, pp. 388–404, 2011

work page 2011

[6] [3]

Mobile robot trajectory planning for large volume data-muling from wireless sensor nodes,

O. Tsilomitrou, A. Tzes, and S. Manesis, “Mobile robot trajectory planning for large volume data-muling from wireless sensor nodes,” in 25th Mediterranean Conference on Control and Automation (MED). Valletta, Malta: IEEE, July 2017, pp. 1005–1010

work page 2017

[7] [4]

Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines,

A. A. Somasundara, A. Ramamoorthy, and M. B. Srivastava, “Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines,” in 25th IEEE International Real- Time Systems Symposium . IEEE, December 2004, pp. 296–305

work page 2004

[8] [5]

D. L. Applegate, R. E. Bixby, V . Chvatal, and W. J. Cook, The traveling salesman problem: A computational study . Princeton University Press, 2011

work page 2011